Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"8362",leadTitle:null,fullTitle:"Time Series Analysis - Data, Methods, and Applications",title:"Time Series Analysis",subtitle:"Data, Methods, and Applications",reviewType:"peer-reviewed",abstract:"This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.",isbn:"978-1-78984-779-6",printIsbn:"978-1-78984-778-9",pdfIsbn:"978-1-78984-786-4",doi:"10.5772/intechopen.78491",price:119,priceEur:129,priceUsd:155,slug:"time-series-analysis-data-methods-and-applications",numberOfPages:130,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"7e98dd03d921c19cc2324e91845d5160",bookSignature:"Chun-Kit Ngan",publishedDate:"November 6th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/8362.jpg",numberOfDownloads:11656,numberOfWosCitations:4,numberOfCrossrefCitations:23,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:36,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:63,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 7th 2018",dateEndSecondStepPublish:"June 28th 2018",dateEndThirdStepPublish:"August 27th 2018",dateEndFourthStepPublish:"November 15th 2018",dateEndFifthStepPublish:"January 14th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"227503",title:"Dr.",name:"Chun-Kit",middleName:null,surname:"Ngan",slug:"chun-kit-ngan",fullName:"Chun-Kit Ngan",profilePictureURL:"https://mts.intechopen.com/storage/users/227503/images/system/227503.jpeg",biography:"Dr. Chun-Kit Ngan is an Assistant Teaching Professor of Data Science Program at Worcester Polytechnic Institute (WPI) since January 2018. Before joining WPI, he was an Assistant Professor in the Division of Engineering and Information Science at the Pennsylvania State University-Great Valley (PSU-GV). His research interests are Decision Guidance and Support Systems (DGSS), including Decision Optimization Models, Computational Algorithms, Machine/Deep Learning, Data Analytics and Visualization, Natural Language Processing, Computer Vision, and related DGSS Applications, to guide domain-specific decision makers to make better decisions and provide them with actionable recommendations. He has published over 30 articles in various books, journals, and conferences. He received the Best Paper Award and the Best Student Paper Award at the 2013 and 2011 International Conference on Enterprise Information Systems respectively. His co-authored paper was selected as the Best Presentation Award in the Information Theory and Technology Session at the 2019 International Conference on Information System and Data Mining. He was the recipient of the 2013-2014 Seed Money Research Grant and 2015-2016 Early Career Award for Research and Scholarship Excellence at PSU-GV. He is also the Co-PI of the funded 2019-2023 NSF REU Site Data Science for Healthy Communities in the Digital Age at WPI, the Co-PI of Developing a Prognostic Model to Predict Inter-individual Trajectory of Functional Outcomes in Adolescents and Young Adults with Cancer funded by Hong Kong Research Grants Council in 2022-2023, and the PI of the Clinical Decision Support System (CDSS) for Pediatric Hypertension project funded by Diameter Health, Inc in Spring 2022. The CDSS project team has won the first runner-up prize of the Student Poster Contest at the 2022 New England HIMSS Annual Spring Conference. In Summer 2022, he joined the Software Engineering team as a Visiting College Educator at Microsoft Corporation. Other DGSS-related research projects have been technically supported by Vodafone GmbH and John Snow Labs, Inc. Publications: (1) ResearchGate - https://www.researchgate.net/profile/Chun-Kit-Ngan and (2) Google Scholar - https://scholar.google.com/citations?hl=en&user=CaNoXysAAAAJ&view_op=list_works&alert_preview_top_rm=2&sortby=pubdate",institutionString:"Worcester Polytechnic Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Worcester Polytechnic Institute",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"977",title:"Mathematical Modeling",slug:"mathematics-statistics-mathematical-modeling"}],chapters:[{id:"66836",title:"Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series",doi:"10.5772/intechopen.85456",slug:"process-fault-diagnosis-for-continuous-dynamic-systems-over-multivariate-time-series",totalDownloads:1660,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be extended in multiple ways to time series analysis.",signatures:"Chris Aldrich",downloadPdfUrl:"/chapter/pdf-download/66836",previewPdfUrl:"/chapter/pdf-preview/66836",authors:[{id:"238414",title:"Prof.",name:"Chris",surname:"Aldrich",slug:"chris-aldrich",fullName:"Chris Aldrich"}],corrections:null},{id:"65010",title:"Fuzzy Forecast Based on Fuzzy Time Series",doi:"10.5772/intechopen.82843",slug:"fuzzy-forecast-based-on-fuzzy-time-series",totalDownloads:1600,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter mainly uses fuzzy time series for interval prediction and long-term significance level analysis. In this study, the Taiwan Shipping and Transportation Index (Taiwan STI) is used to illustrate the prediction process. Nine steps have been used to establish the interval prediction of the Taiwan Shipping and Transportation Index (Taiwan STI), and ΔS is called a long-term significance level (up/down/stable) is used to illustrate the long-term prediction significance level. By means of interval prediction and long-term prediction significance level, the future trends for this index and more internal messages related to this index can be provided to relevant researchers.",signatures:"Ming-Tao Chou",downloadPdfUrl:"/chapter/pdf-download/65010",previewPdfUrl:"/chapter/pdf-preview/65010",authors:[{id:"262691",title:"Prof.",name:"Ming-Tao",surname:"Chou",slug:"ming-tao-chou",fullName:"Ming-Tao Chou"}],corrections:null},{id:"66346",title:"Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting",doi:"10.5772/intechopen.85457",slug:"training-deep-neural-networks-with-reinforcement-learning-for-time-series-forecasting",totalDownloads:2761,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:1,abstract:"As a kind of efficient nonlinear function approximators, artificial neural networks (ANN) have been popularly applied to time series forecasting. The training method of ANN usually utilizes error back-propagation (BP) which is a supervised learning algorithm proposed by Rumelhart et al. in 1986; meanwhile, authors proposed to improve the robustness of the ANN for unknown time series prediction using a reinforcement learning algorithm named stochastic gradient ascent (SGA) originally proposed by Kimura and Kobayashi for control problems in 1998. We also successfully use a deep belief net (DBN) stacked by multiple restricted Boltzmann machines (RBMs) to realized time series forecasting in 2012. In this chapter, a state-of-the-art time series forecasting system that combines RBMs and multilayer perceptron (MLP) and uses SGA training algorithm is introduced. Experiment results showed the high prediction precision of the novel system not only for benchmark data but also for real phenomenon time series data.",signatures:"Takashi Kuremoto, Takaomi Hirata, Masanao Obayashi, Shingo Mabu and Kunikazu Kobayashi",downloadPdfUrl:"/chapter/pdf-download/66346",previewPdfUrl:"/chapter/pdf-preview/66346",authors:[{id:"14090",title:"Prof.",name:"Masanao",surname:"Obayashi",slug:"masanao-obayashi",fullName:"Masanao Obayashi"},{id:"15895",title:"Assistant Prof.",name:"Takashi",surname:"Kuremoto",slug:"takashi-kuremoto",fullName:"Takashi Kuremoto"},{id:"208510",title:"Prof.",name:"Shingo",surname:"Mabu",slug:"shingo-mabu",fullName:"Shingo Mabu"},{id:"208511",title:"Prof.",name:"Kunikazu",surname:"Kobayashi",slug:"kunikazu-kobayashi",fullName:"Kunikazu Kobayashi"},{id:"283417",title:"Dr.",name:"Takashi",surname:"Kuremoto",slug:"takashi-kuremoto",fullName:"Takashi Kuremoto"}],corrections:null},{id:"64216",title:"CNN Approaches for Time Series Classification",doi:"10.5772/intechopen.81170",slug:"cnn-approaches-for-time-series-classification",totalDownloads:3394,totalCrossrefCites:20,totalDimensionsCites:28,hasAltmetrics:1,abstract:"Time series classification is an important field in time series data-mining which have covered broad applications so far. Although it has attracted great interests during last decades, it remains a challenging task and falls short of efficiency due to the nature of its data: high dimensionality, large in data size and updating continuously. With the advent of deep learning, new methods have been developed, especially Convolutional Neural Network (CNN) models. In this paper, we present a review of our time series CNN approaches including: (i) a data-level approach based on encoding time series into frequency-domain signals via the Stockwell transform, (ii) an algorithm-level approach based on an adaptive convolutional layer filter that suits the time series in hand, and (iii) another algorithm-level approach adapted to time series classification tasks with limited annotated data, which is a global, fast and light-weight framework based on a transfer learning technique with a source learning task similar or different but related to the target learning task. These approaches are implemented on identifying human activities including normal movements of typical subjects and disorder-related movements such as stereotypical motor movements of autistic subjects. Experimental results show that our approaches improve performance of time series classification.",signatures:"Lamyaa Sadouk",downloadPdfUrl:"/chapter/pdf-download/64216",previewPdfUrl:"/chapter/pdf-preview/64216",authors:[{id:"257943",title:"Ph.D.",name:"Lamyaa",surname:"Sadouk",slug:"lamyaa-sadouk",fullName:"Lamyaa Sadouk"}],corrections:null},{id:"66426",title:"Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis",doi:"10.5772/intechopen.85458",slug:"forecasting-shrimp-and-fish-catch-in-chilika-lake-over-time-series-analysis",totalDownloads:1189,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Chilika lagoon (a Ramsar site) is a large source of fish production and biodiversity situated in the east coast of India, Odisha. Shrimp landings contribute an average of 4185 MT (Metric Ton) around 35% of total fish production. In this study, SARIMA (Seasonal Auto Regressive Integrated Moving Average) model has been developed on quarterly time series shrimp catch data during the year 2001–2015 and forecasted up to 2018. The best model was selected on Akaike Information Criteria (AIC) and Bayesian Information Criterion (SBC). Results showed that maximum average shrimp landings were observed in the first quarter period (summer season), whereas maximum variation in catch was observed during second quarter Q2 (monsoon season) and lowest variation in the fourth quarter Q4 (winter season) catch during the year 2001–2015. The developed time series SARIMA (0,1,1)(0,1,1)4 model was found to be the best fitted model for the shrimp landings in the lagoon. This article also delineates the application of SARIMAX model (SARIMA with regressors) using monthly catch prediction of fisheries in the Chilika Lake. The developed model is validated with less than 10% errors showing increasing fish catch in the upcoming years by maintaining the present lake condition.",signatures:"Rohan Kumar Raman and Basanta Kumar Das",downloadPdfUrl:"/chapter/pdf-download/66426",previewPdfUrl:"/chapter/pdf-preview/66426",authors:[{id:"265048",title:"Dr.",name:"Rohan",surname:"Raman",slug:"rohan-raman",fullName:"Rohan Raman"},{id:"265049",title:"Dr.",name:"Basanta",surname:"Das",slug:"basanta-das",fullName:"Basanta Das"}],corrections:null},{id:"65250",title:"Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development",doi:"10.5772/intechopen.83801",slug:"using-gray-markov-model-and-time-series-model-to-predict-foreign-direct-investment-trend-for-support",totalDownloads:1055,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Foreign direct investment (FDI) is one of the important factors affecting China’s economic development, the prediction of which is the basis of its development and decision-making. Based on elaborating the significant role in China’s economic growth and the status quo of utilizing foreign investment over the period between 2000 and 2016, this chapter attempts to construct Gray-Markov model (GMM) and time series model (TSM) to forecast the trend of China’s utilization of FDI and then compares the precision of two different prediction models to obtain a better one. Results indicate that although it is qualified, traditional Gray model needs to be optimized; GMM is built to help modify the result, improve Gray-related degrees, and narrow the gap with real value. Comparing the accuracy of GMM with that of TSM, we can conclude that the fitting effect of GMM is better. To increase the credibility of these results, this chapter is based on the data of Beijing and Chongqing from 1990 till 2016, also verifying that the fitting effect of GMM is superior to that of the TSM. Then, we can safely draw a conclusion that the prediction model of GMM is more credible, which has a certain referencing value for the utilization of FDI.",signatures:"Yanyan Zheng, Tong Shu, Shou Chen and Kin Keung Lai",downloadPdfUrl:"/chapter/pdf-download/65250",previewPdfUrl:"/chapter/pdf-preview/65250",authors:[{id:"262190",title:"Prof.",name:"Tong",surname:"Shu",slug:"tong-shu",fullName:"Tong Shu"},{id:"262200",title:"Dr.",name:"Yanyan",surname:"Zheng",slug:"yanyan-zheng",fullName:"Yanyan Zheng"},{id:"262201",title:"Prof.",name:"Shou",surname:"Chen",slug:"shou-chen",fullName:"Shou Chen"},{id:"262202",title:"Prof.",name:"Kin Keung",surname:"Lai",slug:"kin-keung-lai",fullName:"Kin Keung Lai"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5856",title:"Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical 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1. Introduction
Object tracking is defined as a problem of estimating the object’s trajectory, done by means of a video image. There are several tools for tracking objects and are used in various fields of research, such as computer vision, digital video processing, and autonomous vehicle navigation [1]. With the emergence of high-performance computers, high-resolution cameras, and the growing use of so-called autonomous systems that, in addition to these items, require specialized tracking algorithms, increasingly accurate and robust for automatic video analysis, has currently been the target of numerous research on the development of new object tracking techniques [2, 3].
Object tracking techniques are applicable to motion-based reconnaissance cases [4], automatic surveillance systems [5], pedestrian flow monitoring in crosswalks [6], traffic control [7], and autonomous vehicular navigation [8]. Problems of this type are highly complex due to the characteristics of the object and the environment, generating many variables, which impairs performance and makes the application of tracking algorithms unfeasible to real-world situations. Some approaches seek to resolve this impasse by simplifying the problem, reducing the number of variables [9]. This process, in most cases, does not generate good results [10, 11], making it even more difficult to identify the main attributes to be selected to perform a task [12, 13].
Most of the object tracking problems occur in open environments, so-called uncontrolled [14]. The complexity of these problems has attracted the interest of the scientific community and generated numerous applied research in various fields of research. Current approaches, such as the ones that use convolutional neural networks—CNN, deal well with the high number of variables of these types of problems, providing space–temporal information of the tracked objects, through three-dimensional convolutions [15, 16, 17]. This ends up creating an enormous number of learnable parameters, which ends up generating an overfitting [11]. A solution to reduce this number of learnable parameters was combining space–time data, extracted using the optical flow algorithm, used in the Two-Stream technique [18, 19, 20]. However, this technique presents good results only for large datasets, showing itself to be inefficient for small datasets [15, 21].
In recent years, research using machine learning has been applied to tracking problems, gaining notoriety due to the excellent results obtained in complex environments and attribute extraction [21, 22, 23]. Deep learning stands out among these techniques for presenting excellent results to unsupervised learning problems, [24], object identification [25], semantic segmentation [26]. Random trees are also examples of machine learning techniques, and their excellent results, due to their precision and great capacity to handle a large volume of data and low overfitting tendency [27, 28], and widely used in research areas such as medicine, in the prediction of hereditary diseases [29], agriculture to increase the productivity of a given plantation crop and in astronomy, acting on the improvement of images captured by telescopes, in the spectrum electromagnetic radiation not visible to the human eye [30]. The possibilities of applications, and new trends and research related to machine learning techniques, with particular attention to random trees, allow the development of algorithms that can be combined with existing ones, in the case of optical flow algorithms, (belonging to computational field of view) taken advantage of in this way, the advantages of each [31, 32, 33].
Developing an algorithm whose objective is to track objects, using the particular advantages of these techniques in a combined way, justifies creating a tracking algorithm that combines the optical flow technique, adapted in this work in terms of the Gaussian curvature associated with a minimal surface, with a random trees waiting for it to capture on this surface a minimum number of optical flow vectors that characterize the moving object, accurately and with low computational cost, contributing not only in the fields of computational vision but in other branches of science, such as in medicine, it can help in the early identification of infarctions.
2. Related works
Due to the large number of studies related to the technique of object tracking, only a small number surrounding this theme will be addressed. The focus of this project is not to make a thorough study on the state of the art. With this in this item, the main works in the literature, associated with the tracking of objects, will be presented. Among the various approaches used for this context, we highlight those focused on the techniques of optical flow, and others belonging to machine learning, such as those that use identifications of patterns, which allow relating, framing, and justifying the development of this proposal and its importance, through its contribution, to the state of the art.
2.1 Object tracking
Object tracking is defined as a process that allows you to uniquely estimate and associate the movements of objects with consecutive image frames. The objects considered can be from one, the set of pixels belonging to a region of the image. The detection of pixels is done by a motion detector or objects, which allows to locate objects with similar characteristics that move, between consecutive frames.
These characteristics of the object to be tracked are compared with the characteristics of a reference object modeled by a classifier over a limited region of the so-called region of interest frame, where the probability of detection of the object is greater. Thus, according to [33], the detector of traced objects, locate several objects on the different parts of the region of interest and performs the comparison of these objects with the reference object. This process is performed for each frame and each object detected, candidate to be recognized as the greatest possible similarity, to the reference object can be represented, through a set of fixed-size characteristics, extracted from this region containing a set of pixels, which can be represented by a numerical array of data.
Thus, mathematically, the region containing a set of pixels belonging to the regions of the object of interest, where the characteristics that allow to test whether the region of the frame, in which the object to be traced is, is given by:
OCit=OCt∣∥LOCt−LORi−1∥<εE1
where, LOCt is the position xy of the centroid of the candidate object OCt, LORi−1, is the position of the object traced to the i–1—frame of the video and 0<ε∈R, is an actual value associated with the size of the region of the object of interest.
According to the works of [34, 35], learning methods are used to adapt the changes of movement and other characteristics such as geometric aspect and appearance of the tracked object. These methods are usually used adaptive tracked object trackers and detectors. The following will be presented other types of object trackers, found in the literature.
According to [36], a classifier can be defined with a f belonging to a family of functions F parameterized by a set of classifier parameters. They form a detector of objects to be tracked which in turn is an integral part of a tracker. A classifier can also be training and thereby generate a set of classification parameters, producing the function f, that allows you to efficiently indicate the classes vi of the test data xi from a training set Ct={x1y1,…,xnyn}. The data is points in the space of the characteristics, which can be entropy, the gray level, among others.
The classifier aims to determine the best way to discriminate the data classes, on the space of characteristics. The test data form a set containing the characteristics of the candidate objects, which have not yet been classified. The position of the object to be tracked in the frame is defined as the position corresponding to the highest response of the detector of the object to be tracked on the ith-candidate objects. Therefore, the position of the object to be tracked is determined by the position of the ith-candidate object, which is most likely to belong to the class of the crawled object, given by the following equation:
where the variable COR in the equation (3), are the classes of the tracked objects and the candidate objects OC, all with equiprobability of occurrence. According to the types of classifiers used object detectors to be tracked, along with the initial detector, it is possible to use some learning technique to train them. One of the ways used is offline training [36] and adjusting the parameters of the classifier before running the tracker.
Offline-trained classifiers are generally employed in object detectors designed to detect all new objects of interest that enter the camera’s field of view [37]. The training set Ct, must contain characteristics xi extracted from the objects to be traced and diverse environmental characteristics. This allows new objects, with varied geometric characteristics and aspects, to be detected more efficiently. As for online training, the adjustment of the parameters of the classified is performed during the tracking process. For online trained classifiers, they are generally used in object detectors to be tracked. Thus, in each frame, the new extracted characteristics are used to adjust the classifiers.
2.1.1 Binary classification
In [38], trackers that use the detection tracking technique deal with object tracking, as a binary classification problem whose goal is to find the best function f that separates the objects to be tracked R, of other objects in the environment. Object tracking seen as a binary classification problem, which is currently one of the subjects that receives the most attention in research in computing vision.
In [39], were developed trackers that used detectors of objects to be tracked, formed by classifiers in committee formed by binary classifiers said weak. For [40], a binary classifier is defined as a classifier, used in problems where the class yi of a OCi belongs to the set Y=−11. The negative class {−1} refers to the characteristics of the environment and other objects. A positive class {+1} refers to the class of the object to be tracked.
A classifier is said to be weak, when it has a probability of “hitting” a given data class, only slightly higher than a random classifier. The detector of the object to be tracked must separate the crawled object from the other objects and the environment. Its purpose and determine the position of the tracked object, according to the equations (1)–(3). According to [41, 42] each of the ithcandidate object classes OCi, it is defined according to Bayesian theory of decision, through minimal classification error. This means that, the decision given by observing, the sign of the difference between Pyi=COROCit,PRt and Pyi=CNOROCit,PRt, so that the sum of these probabilities is unitary.
2.1.2 Monitoring systems
For [43], the term monitoring system, refers to the process of monitoring and autonomous control, without human intervention. This type of system has the function of detecting, classifying, tracking, analyzing, and interpreting the behavior of objects of interest. In [44, 45], this technique was used combined with statistical techniques for controlling people’s access to a specific location. It was also observed the use of intelligent monitoring systems, applied to building, port, or ship security [46, 47].
The functions comprised by a monitoring system are so-called low- and high-level tasks. Among some high-level tasks, we highlight the analysis, interpretation and description of behavior, the recognition of gestures, and the decision between the occurrence or not of a threat. Performing high-level tasks require that for each frame, the system needs to perform low-level tasks, which involve direct manipulation of the image pixels [48, 49, 50, 51, 52, 53, 54, 55, 56]. As an example, we highlight the processes of noise elimination, detection of connected components, and obtain information on the location and geometric aspect of the object of interest.
A monitoring system consists of five main components, which are presented in Figures 9. Some monitoring systems may not contain all components. The initial detector aims to detect the pixel regions of each frame that have a significant probability of containing an object to be tracked. This detector can be formed by a motion detector that detects all moving objects based on models of objects previously recorded in a database or based on characteristics extracted offline [40, 41]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
The regions containing the most relevant pixels are analyzed and then classified as objects of interest by the classifier [50, 51, 52, 53, 54]. Objects of interest are modeled and are now called reference objects so that the tracker determines its position frame by frame [55, 56]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
A tracker, an integral part of a detector, is defined as a function that allows estimating the position of objects at each consecutive frame, through and defines the region of the object of interest, for each ith object being tracked within a region of interest. This estimation of the movement is performed through the correct association of the captured and tracked objects, to consecutive video frames. The trace often and interpreted as a data binding problem. Figure 1 shows a schematic of the main components of a monitoring system.
Figure 1.
Main component of a monitoring.
2.2 State of the art in object tracking with optical flow
Several techniques that allow the calculation to have been developed in recent years to calculate the optical flow vector [57]. These methods are grouped according to their main characteristics and the approach used for the calculation of the optical flow. Thus, the differential methods performed in the studies in [56], the methods d and calculation of the optical flow through the frequency domain [46] the phase correlation methods [58], and the method of association between regions [59].
The method proposed in [56], allows the calculation of the optical flow for each point around a neighborhood of pixels. In [60], it is also considered a neighborhood of pixels, but in this case, the calculation of the optical flow is performed geometrically. In the work presented by [61] it is adding of the restrictions of regularization. In [62] turn active compare performance analyses were performed between the various algorithms and optical flow present in the literature.
This technique is considered robust for detaining and tracking moving objects from your images, both those captured by fixed or mobile cameras. This gives this technique, but high computational cost makes most practical applications unfeasible. Thus, to reduce this complexity, techniques of increasing resolutions were adopted in [63]. Also, for the same purpose, we used the techniques of subsampling on some of the pixels belonging to the object of interest to obtain optical flow [52].
Other authors also use a point of interest detector to select the best pixels for tracking and calculate the optical flow on these points [52, 64]. The reduction in the number of points to be tracked is associated with a decrease in computational complexity, so in [52] the points of interest were selected using the FAST algorithm [64].
The method developed by Lucas-Kanade [56], it is a differential method and widely used in the literature and having variations modifications. It allows you to estimate the optical flow for each point xyt calculating the like transformation (TAxt, applied to the pixels of a pixel grid, with center in xy by the following function fxt, that is:
fxt=min∑x∈pixel gridQxt−1−QTAxt∗gxE4
where gx is a Gaussian smoothing filter centered on x.
New variations of the techniques were being proposed to make the calculation of the optical flow faster and faster. In [65] a tracker was proposed based on the algorithm of [56]. The translation of a point represented by a grid of rectangular sized pixels 25 × 25, was calculated and its validity is evaluated by calculating the SSD1 in the grid pixels in Qt and in Qt−1. If the SSD is high, the point is dropped and stops being traced.
In [51] objects were detected by subtracting the image from the environment and removed the movement of the camera with the calculation algorithm of the optical flow vector proposed by [56]. In the studies carried out in [66, 67], they showed that the reliability of the estimated optical flow reduced the case of some points of the object of interest whose optical flow cannot be represented by the same matrix given by the related transformation TAxt of the other points. Thus, to improve the robustness of the algorithm of [56, 67] proposed a calculation of the independent optical flow vector for each of the N points belonging to the object of interest selected with the SURF (Speeded Up Robust Features) point detector in the initial frame.
In [67] they also modified Lucas - Kanade’s algorithm [56] by inserting the Hessian matrix in the calculation of the value of the variation of the related transformation ΔTAxt. The algorithm allows for more effective tracking when partial occlusions, deformations, and changes in lighting occur, as optical flow is not calculated considering all points of objects of interest.
Already in the proposal presented in [68] was the development of algorithm to detect people in infrared images that combines the information of the value of pixels with a method of motion detection. The algorithm forms a relevant pixel map by applying thresholding segmentation. While the camera is still, an image M is built with the differentiation between frames. If the camera is in motion, M is filled with the pixels obtained by the analysis of the moment of the optical flow calculated by the algorithm of [56]. The map of relevant pixels is replaced by the union between M and the Pixel Map relevant to the first case and the second an interception between M and the pixel map relevant case to compensate for the movement of the camera.
The method for tracking swimmers presented in [46], uses the information of the movement pattern by the optical flow and the appearance of the water that is modeled by a MoG.2 This allows you to calculate an optical flow vector for each pixel of the video independently of the other, through B which is an array composed of gradients in the directions xand ypixels in a grid of pixels.
In [69], a method was presented that incorporated physical restrictions to the calculation of optical flow. The tracker uses the constraints to extract the moving pixels with a lower failure rate. The calculation can be impaired when occlusions occur or when the environment has low light. The operator defines the physical constraints and selects the points of the OR that are tracked by optical flow. Constraints can be geometric, kinematic, dynamic, of the property of the material that makes up the OR or any other type of restriction.
In [70], the points that are tracked with the optical flow are defined by applying the Canny edge detector on the pixels of the reference pixel map. Pixels that produce a high response to the Canny detector are the selected points.
In [43], optical flow is used as a characteristic for tracking the contour of the object. The contour is shifted in small steps until the position in which the optical flow vectors are homogeneous is found.
In [64], they performed an estimate of the translation and orientation of the reference object by calculating the optical flow of the pixels belonging to its silhouette. The coordinates of the centroid position are defined by minimizing the Hausdorff distance between the mean of the optical flow vectors of the reference object and the candidate object to be chosen as the object of interest.
2.2.1 Optical flow as a function of Gaussian curvature
Optical flow is defined as a dense vector field associated with the movement and apparent velocity of an object, given by the translation of pixels from consecutive frames in an image region. It can be calculated from the brightness restriction, considered constant, from the corresponding pixels in consecutive frames.
Mathematically be a pixel xy, associated with a luminous intensity Ixy, over an image surface or plane, and a time interval and a sequence of frames associated with an apparent offset of the pixel over that image surface or plane. Thus, the rate of variation of light intensity in relation to a time interval, associated with the apparent movement of the pixel, on a surface or plane of the image, being considered practically null can be given by:
dIxydt=∂Ixy∂xdxdt+∂Ixy∂ydydt+∂IuvdtE5
=Ixux+Iyvx+ItE6
dIxydt=0⟹Ixux+Iyvx+It=0E7
So that equation (7) is called optical flow restriction and where the terms Ix, Iy, It denote the derivatives relative to the brightness intensity relative to the coordinates x,y and time t, and u and v, uxyvxy are the horizontal and vertical components of a vector representing the optical flow field, for the pixel xy in question.
The number of variables in equation (6) is greater than that of equations, which does not allow estimating components and vector, and determining a single solution for the optical flow restriction equation. With this, Lucas and Kanade proposed a solution to solve this problem. The solution method proposed by them considers the constant flow in a region formed by a set of pixels N×N, so you can write the optical flow restriction equation for each pixel in this region, thus obtaining a systems of equations with 2 variables, that is:
Passing the set of equations given by equation (8) to the matrix form we have:
Ix1Iy1⋮⋮IxpIypvxvy+It1⋮Itp=0⋮0E9
Using the least squares method, in the system of equations (9) in the form of matricial, the same can be solved. Therefore, the optical flow v=vxvy can be estimated for a particular region or window with N×N pixels, that is:
This method has a reduced computational cost to determine optical flow estimation when compared to other methods because it is simple, that is, it is since the region in which the variation of light intensity between pixels is minimal has a size 2×2, contained in a region NxN. In this way, the Optical Flow is determined in a region of 2×2, between these two pixels, using only one matrix inversion operation (equation (12)).
To calculate the optical flow over the size region NxN, partial derivatives must be calculated in each pixel. However, considering almost null the variation of the intensity of light between pixels, over the region, the small differences in the accumulated intensities of brightness between pixels compromise the accuracy of the Optical Flow in relation to the determination of the actual motion object, that is, it gains in the processing speed and loses precision in the determination of the motion. When deriving equation (5) we have equation (13), that is:
ξα2=axα1+ayα2+a3u2+a4v2+a5uv+a6u+a7v+a8E13
Where the terms αx=∂vx∂t, αy=∂vy∂t, are called the components of the acceleration vector, vxvy is the components of the velocity vector and the terms a1=Ix=∂∂xIxyt;a2=Iy=∂∂yIxyt;a3=Ixx=∂2∂x2Ixyt;a4=Iyy=∂2∂x2xyt;a5=Ixy=∂∂xyIxyt;a6=Ixt=∂∂xtIxyt; a7=Iyt=∂∂ytIxyta8=Itt=∂∂t2Ixyt, are the first and second partial derivatives of the Ixyt.
In view of the small variations present and accumulated along the vector field associated with the optic flow, which cause an additional error in equation (13), a regularization adjustment was made, given by equation (14):
where α is the value of the weights required for smoothing the variation of the associated optical flow. So, to get the vx=vxxy e vy=vyxy, thus using the resources of the variational calculation one has:
2α3vx+α5vy=α2∇2vx−b1α5vx+2α4vy=α2∇2vy−b2E16
where ∇2vx is the Laplacian of vx e ∇2vy is the Laplacian of vy and the coefficients de b1, b2 can be given as:
b1=∂∂tα1+α6b2=∂∂tα2+α7E17
and replacing the coefficients Ix, Iy, Ixx, Iyy, Ixy, Ixt, Iyt, Itt in equation (16), one has:
Ixxvx+Ixyvy=α23∇2vy−IxtE18
Ixyvx+2Ixyvy=α23∇2vx−IytE19
whereas ∇2vx=vx1jk¯−vx1jk, e ∇2vy=vy1jk¯−vj1jk, are the Laplacians of equations (18) and (19), given in their discretized digital forms together with equation (20),
λ=α23E20
It is possible to reduce the data system by (17), such as:
λ2Ixx+Iyy+λ2+κvx=λ2Iyy+λ2vx¯−λ2Ixy+vx¯+c1E21
λ2Ixx+Iyy+λ2+κvx=−λ2(Iyyvx¯+λ2Iyy+λ2vy¯+c2E22
where the term κ=IxIyy−Ixy2, it is called Gaussian curvature of the surface. And it is also that:
The Algorithm 1 is a pseudocode to generate the proposed optical flow vector, through equations (24) and (25) and that allow estimating the speed and position of an object, through a sequence of video images.
Calculate the discretized Laplacians of ∇2vx, ∇2vy
Calculating Gaussian curvature κ
Calculate flow components (u, v)
End For
End
2.2.2 Random forests
Developed by Breiman [63] in the mid-2000s, and later revised in [71] random trees are considered one of the best-supervised learning methods used in data prediction and classification. Due to its simplicity, low computational cost, great potential to deal with a large volume of data, and still present great accuracy of results, currently this method has become very popular being applied in various fields of science as data science [72]. Bioinformatics, Ecology, in real-life systems and recognition of 3D objects. In recent years, several studies have been conducted with the objective of making the technique more elaborate and seeking new practical applications [73, 74, 75].
Many studies were carried out with the aim of narrowing the existing gap between theory and practice can be seen in [58, 76, 77, 78]. Among the main components of random tree forests, one can highlight the bagging method [63], and the criterion of classification and regression called cart-split [79], which play critical roles.
Bagging (a bootstrap-aggregating contraction) is an aggregation scheme, which generates samples through the bootstrap method, from the original dataset. These methods are nonparametric and belong to the Monte Carlos method class [80], treating the sample as a finite population. Still, these methods are used when the distribution of the target population is not specified, and the sample is the only information available. How in this way a predictor of each sample is constructed, so that the decision is made through an average, and is more effective computational procedures to improve the indexable estimates, especially for large sets of high-dimensional data, where finding a good model in one step is impossible due to the complexity and scale of the problem. As for the cart-split criterion, it originates from the CART program [63], and is used in the construction of individual trees to choose the best cuts perpendicular to the Axes. However, while bagging and the CART division scheme are key elements in the random forest, both are difficult to mathematically analyze and are a very promising field for both theoretical and practical research.
In general, the set of trees is organized in the form of {T1 (Ɵ1), T2 (Ɵ2)… Ti (Ɵi}, where TB is every tree and ƟB are bootstrap samples with spare dimensions qx mtry, where mtryis equal to the number of variables that will be used on each node during the construction of each tree and qis approximately 0,67×n. Each of the trees produces a response y1,i for each of the samples W {T1W = y1,i, T2W = y2,i, .., TiW = y2,B} and the mean (regression) or majority vote (classification) of the tree responses will be the final response of the model for each of the samples.
3. Methodology
The methodology employed consisted of combining the optical flow algorithm in terms of Gaussian curvature, developed in this work together with the technique of random forest. The language used for the development of this algorithm was the MATLAB programming language, executed on a 64-bit 8th generation notebook, CORE i7. The input data is a video extension Avi, lasting 5 min of a vehicle and two cyclists, circulating in the vicinity of the beach of Costa Nova, in the locality of Ilhavo, in Aveiro, Portugal. The video was fragmented into a set of frames, analyzed two by two by the algorithm for the generation of the vector field of optical flow. After that, the resulting image associated with the flow and a minimal surface region, given by the Gaussian curvature. Next on this surface, the random trees analyzed which vectors presented important characteristics to characterize in an “optimal” way, the movement of the object (see Figure 2).
Figure 2.
Representative model of operation of a random forest.
After finishing the process of analysis of the movement of the objects, the execution times and accuracy of the results obtained by the proposed algorithm were compared in relation to the algorithms of Lucas Kanade, Horn and Shunck, Farneback and Lucas Kanade with or without Gaussian filter, allowing to validate the results obtained. After that, the implementation of the developed algorimo began.
4. Results
Figure 3 shows the vehicle and the two cyclists that were used to collect the image to which the results proposed in this work were obtained so that the choice was random on the right side. A graphical representation of the vector field of optical flow generated by the sequence of two consecutive frames, over 5 minutes of video is shown.
Figure 3.
(a) Left side: vehicle shift between moments t−1 and t. (b) Right side: representation of the corresponding optical flow.
On the right side of Figure 3, the optical flow associated with the movement of the vehicle between the time intervals from t−1 to t is being represented. Note that the vector representation of this field associated with this flow was performed in such a way that the vectors generated by the field were superimposed in the horizontal direction of the central axis of the figure. Although there were other objects present at the site, that is, two cyclists and a car in the upper left corner, the object of interest considered was the vehicle close to the cyclists. This is shown on the right side of Figure 3, by the layout of this horizontal arrangement of vectors, which allows indicating whether the current movement and the predicted movement of the considered object is to the left or to the right.
The region with the highest horizontal vector density in Figure 3 is located on the left side, in blue. It is also observed that the number of vectors in this region, despite being spaced, starting from the center to the left, is greater in relation to the number of vectors on the right side. It is also possible, through it, to visually evaluate the movement behavior of the considered objects. This region, containing a higher vector density, corresponds to the current direction in which the object is heading and its predicted displacement. It is also possible to observe that this vector density increases towards the left side, passing through the central part, coming from the right, clearly indicating the direction of movement of the object, that is, the object moves to the left. In Figure 4, this process can be understood more clearly.
Figure 4.
Prediction and actual displacement of the object obtained through the optical flow.
In a similar way to the one mentioned in Figure 3, on the right side of Figure 5, the optical flow generated by the displacement of the moving vehicle is represented, between the instants t to t+1. It is possible to observe that the vector representation of this field was performed in such a way that its vectors representing this field were also superimposed on the axis in the horizontal direction of the figure, creating a vector density created by this superposition. The object of interest considered remains the vehicle close to the cyclists. As can be seen on the right side of Figure 3, the arrangement of the horizontal vectors also allows to indicate the current movement and, if its movement prediction is to the left or to the right.
Figure 5.
Object remains on the right side, but with a medium offset to the right and displacement estimate still to the left.
It is possible to observe a small increase in the vector density to the left, but that has a great influence on the determination of the real and predicted position of the object in the considered time intervals. The Object continues with its actual movement to the left, as well as the predicted movement of the object to the left. However, he showed a slight movement to the left (direction where the cyclists are).
In Figure 6, a small variation of the optical flow is observed again in the associated movement between the instants t+1 to t+2. In this figure, the vehicle is next to the cyclists, both in the opposite direction to the vehicle. The movement of the vehicle continues without great variation in the direction, causing no period for cyclists or other vehicles in the opposite direction to it on the left side.
Figure 6.
Object remains on the right side, but with a slight shift to the right and offset estimate to the left.
In Figure 7, there was no optical flow variation in the associated movement between the time intervals t+2 to t+3. In this figure, the vehicle can be seen as it passes the two cyclists. This means that the non-significant variation in the optical flow vector field, keeping the number of vectors higher on the left side is associated with maintenance in the direction of movement of the object considered, that is, it continues to move on the left side.
Figure 7.
Object moving and keeping on the left for consecutive frames.
In Figure 8, the vehicle can be seen completely overtaking the two cyclists and approaching another vehicle in the opposite direction in the upper part of the image (left). The variation of the optical flow vector field remains the same. This indicates that the vehicle continues its trajectory, on the left side to the cyclists, however without posing a danger of collision for the other vehicle in the opposite direction.
Figure 8.
Object with unchanged offset pattern.
5. Analysis and discussing the results
This item will show how the performance evaluation of the proposed algorithm and accuracy was performed in relation to the Algorithms of Luca and Kanade, with or without Gaussian filter, Horn and Schunck, and Farneback.
The algorithm allowed to show on the display in real-time the displacement of the object on the right side and the set of vectors capable of representing the movement of the real-time or accumulated indicating the tendency, in this case, of the direction that the object should perform. This process was carried out in a similar way, using the other algorithms to make it possible to compare them. The behavior of the proposed algorithm and the other will be graphically shown.
The technique developed in this work allowed to generate an optical flow considering important geometric properties allowing to identify similar categories of moving objects and same characteristics. These geometric properties are intrinsically associated with the curvature of the object’s surface in three-dimensional space, called Gaussian curvature, in this case in a 2D image.
The modified optical flow, considering these properties, generated a dense optical flow, allowing the generation of a band, describing a track on the 2D plane. This allowed tracking the movement of the considered object. In the same Figure 8, it is possible to observe that at each time interval in which the object was monitored, the dispositions of the vectors for the left and right sides, as shown in Figures 3–7 were responsible for drawing the track associated with the displaced and that allowed tracking the object as it moves.
Figure 9 shows the vehicle that, when moving, generated the optical flow. In Figures 10 and 11, the variations of the optical flow between two-time intervals, Δti and Δtn, (i<n) are shown. In this way, the algorithm allowed tracking the progressive movement of the object (movement adopted as progressive, in this work) and, as this happens, it is possible to predict in which direction it is moving, that is, to the left or to the right (or keeping straight) line.
Figure 9.
Variation of the optical flow of the moving object.
Figure 10.
Vehicle movement.
Figure 11.
Object moving to the left side.
5.1 Optical flow algorithm in terms of the curvature
In the following items, the implementations of the Lucas and Kanade algorithms without or with a Gaussian filter, Horn and Schunck, and Farneback will be shown, using as input data the same sequence of video images used in the algorithm developed in this work. For each, the performance and accuracy obtained will be verified.
5.2 Lucas and Kanade algorithm without Gaussian filter
5.3 Lucas and Kanade Algorithm with Gaussian filter
5.4 Algoritmo de Horn and Schunck
5.5 Algoritmo de Farneback
For each of the 5 algorithms, 1 frame is shown containing 4 figures, with 2 upper and 2 lower. In each frame, the figure at the top left shows the variation of the vector field between two frames. The right frame, on the other hand, corresponds to the variation of the object’s movement in real-time. The lower ones, except for the proposed algorithm, correspond to the number of points on the right or the left, and with this, the movement will occur to the side that has the greatest number of points. In the case of the proposed algorithm, the process will take place through the analysis of vector density. So, to the side where there is greater vector density, this is the side to which the movement will be occurring (see Figures 12–20).
Figure 12.
Object moving to the right side.
Figure 13.
Variation of the optical flow of the moving object.
Figure 14.
Vehicle movement.
Figure 15.
Object moving to the left side.
Figure 16.
Object moving to the right side.
Figure 17.
Variation of the optical flow of the moving object.
Figure 18.
Vehicle movement.
Figure 19.
Object moving to the left side.
Figure 20.
Object moving to the right side.
Comparing the results presented by the algorithms, it is observed that in the developed model, it was possible to see a dense vector trail of the object, with a slight tendency of displacement to the left, as it continues its movement. In the other models, this was not possible, and it is necessary to resort to a score of points, in the lower table. This process is also possible in the proposed model, but not necessary, which means a reduction in computational cost (see Figures 21–28).
Figure 21.
Variation of the optical flow of the moving object.
Figure 22.
Vehicle movement.
Figure 23.
Object moving to the right side.
Figure 24.
Object moving to the left side.
Figure 25.
Variation of the optical flow of the moving object.
Figure 26.
Vehicle movement.
Figure 27.
Object moving to the left side.
Figure 28.
Object moving to the left side.
Comparing the results, it is observed that the Farneback algorithm also presents high vector density. But the proposed model, as previously said, presents a well-defined vector trail which suggests the non-use of the point count in the lower frame, which does not occur for the Farneback algorithm, indicating higher computational cost, which can affect the accuracy of this algorithm when compared to the proposed algorithm.
Comparing the Horn and Schunck algorithm, a low vector density is observed when compared to the proposed algorithm, which indicates lower accuracy when compared to the proposed algorithm.
Although the two techniques of Lucas and Kanade, are faster applications, indicating low computational cost when compared to the proposed algorithm, the factor of low vector density results in low precision in relation to the proposed method.
6. Final considerations
The proposed method presented good results, showing to be accurate and reasonable speed. This allows this application to be used in critical problems, i.e., to real-world problems. However, it presented limitations that could be verified when compared to the model with Lucas and Kanade, with a Gaussian filter, which is faster and presents good accuracy.
The proposed Method reached only approximately 50% execution speed in relation to the Lucas and Kanade Method, which motivates further improvements to the Method. The technique presented can be applied to other fields of research as in cardiology due to presenting great precision when submitted to small region, which is important because it can be applied with the objective of predicting infarctions and as a current contribution, for the state of the art is to characterize the optical flow in terms of Gaussian curvature, that makes it possible to highlight fields of research such as computational vision and differential geometry.
Acknowledgments
The authors of this work would like to thank the Institute of Electronics and Informatics Engineering of Aveiro, the Telecommunications Institute of Aveiro, and the University of Aveiro for the financial, technical-administrative, and structural support provided that allowed the accomplishment of this work.
\n',keywords:"Object tracking, vehicle tracking, optical flow, gaussian curvature, random forest",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81557.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81557.xml",downloadPdfUrl:"/chapter/pdf-download/81557",previewPdfUrl:"/chapter/pdf-preview/81557",totalDownloads:18,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 13th 2021",dateReviewed:"January 25th 2022",datePrePublished:"April 28th 2022",datePublished:null,dateFinished:"April 28th 2022",readingETA:"0",abstract:"The objective of this work is to present an object tracking algorithm developed from the combination of random tree techniques and optical flow adapted in terms of Gaussian curvature. This allows you to define a minimum surface limited by the contour of a two-dimensional image, which must or should not contain a minimum amount of optical flow vector associated with the movement of an object. The random tree will have the purpose of verifying the existence of superfluous vectors of optical flow by discarding them, defining a minimum number of vectors that characterizes the movement of the object. The results obtained were compared with those of the Lucas-Kanade algorithms with and without Gaussian filter, Horn and Schunk and Farneback. The items evaluated were precision and processing time, which made it possible to validate the results, despite the distinct nature between the algorithms. They were like those obtained in Lucas and Kanade with or without Gaussian filter, the Horn and Schunk, and better in relation to Farneback. This work allows analyzing the optical flow over small regions in an optimal way in relation to precision (and computational cost), enabling its application to area, such as cardiology, in the prediction of infarction.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81557",risUrl:"/chapter/ris/81557",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",book:{id:"10652",type:"book",title:"Information Extraction and Object Tracking in Digital Video",subtitle:null,fullTitle:"Information Extraction and Object Tracking in Digital Video",slug:null,publishedDate:null,bookSignature:"Prof. Antonio J. R. Neves and Dr. Francisco J. Gallegos-Funes",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-461-5",printIsbn:"978-1-83969-460-8",pdfIsbn:"978-1-83969-462-2",isAvailableForWebshopOrdering:!0,editors:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. R.",surname:"Neves",slug:"antonio-neves",fullName:"Antonio Neves"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related works",level:"1"},{id:"sec_2_2",title:"2.1 Object tracking",level:"2"},{id:"sec_2_3",title:"2.1.1 Binary classification",level:"3"},{id:"sec_3_3",title:"2.1.2 Monitoring systems",level:"3"},{id:"sec_5_2",title:"2.2 State of the art in object tracking with optical flow",level:"2"},{id:"sec_5_3",title:"2.2.1 Optical flow as a function of Gaussian curvature",level:"3"},{id:"sec_6_3",title:"2.2.2 Random forests",level:"3"},{id:"sec_9",title:"3. Methodology",level:"1"},{id:"sec_10",title:"4. Results",level:"1"},{id:"sec_11",title:"5. Analysis and discussing the results",level:"1"},{id:"sec_11_2",title:"5.1 Optical flow algorithm in terms of the curvature",level:"2"},{id:"sec_12_2",title:"5.2 Lucas and Kanade algorithm without Gaussian filter",level:"2"},{id:"sec_13_2",title:"5.3 Lucas and Kanade Algorithm with Gaussian filter",level:"2"},{id:"sec_14_2",title:"5.4 Algoritmo de Horn and Schunck",level:"2"},{id:"sec_15_2",title:"5.5 Algoritmo de Farneback",level:"2"},{id:"sec_17",title:"6. Final considerations",level:"1"},{id:"sec_18",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Gonzalez RC, Woods RE. Digital Image Processing. 2002'},{id:"B2",body:'Abbass MY, Kwon KC, Kim N, Abdelwahab SA, El-Samie FEA, Khalaf AA. A survey on online learning for visual tracking. The Visual Computer. 2020:1-22'},{id:"B3",body:'Khalid M, Penard L, Memin E. Application of optical flow for river velocimetry. 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IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\\n\\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\\n\\n
\\n\\t
Does your institution already have a budget for covering Open Access publication costs?
\\n\\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\\n
\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7183,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1455,totalCrossrefCites:13,totalDimensionsCites:24,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192987,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4596,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3523,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3609,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. Wydell",authors:[{id:"87489",title:"Prof.",name:"Taeko",middleName:"N.",surname:"Wydell",slug:"taeko-wydell",fullName:"Taeko Wydell"}]},{id:"58597",title:"Testosterone and Erectile Function: A Review of Evidence from Basic Research",slug:"testosterone-and-erectile-function-a-review-of-evidence-from-basic-research",totalDownloads:1349,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Androgens are essential for male physical activity and normal erectile function. Hence, age-related testosterone deficiency, known as late-onset hypogonadism (LOH), is considered a risk factor for erectile dysfunction (ED). This chapter summarizes relevant basic research reports examining the effects of testosterone on erectile function. Testosterone affects several organs and is especially active on the erectile tissue. The mechanism of testosterone deficiency effects on erectile function and the results of testosterone replacement therapy (TRT) have been well studied. Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82319",title:"Traumatic Optic Neuropathy",slug:"traumatic-optic-neuropathy",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104731",abstract:"Traumatic optic neuropathy (TON) is a specific neurological sequence of traumatic brain injury (TBI). It has a different mechanism than other most neurologic complications of head trauma and its consequences can be devastating. The damage can be from direct penetrating trauma or bone fracture injuring the optic nerve directly or secondary to indirect blunt trauma (usually causing traction). The diagnosis of TON is based on the clinical history and examination findings indicative of optic neuropathy, especially the presence of defective pupillary light response. TON can cause only mild vision loss but, in some cases, severe vision loss is present. Imaging findings can support the diagnosis, and provide information on the mechanism as well as treatment options. The treatment options include observation alone, systemic steroids, erythropoietin, surgical decompression of the optic canal, or combination. The evidence base for these various treatment options is controversial and each treatment has its side effects and risks. Poor prognostic factors include poor visual acuity at presentation, loss of consciousness, no improvement in vision in the first 48 hours, and evidence of optic canal fractures on neuroimaging.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Ainat Klein and Wahbi Wahbi"},{id:"82203",title:"Resting-State Brain Network Analysis Methods and Applications",slug:"resting-state-brain-network-analysis-methods-and-applications",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104827",abstract:"Resting-state fMRI has been widely applied in clinical research. Brain networks constructed by functional connectivity can reveal alterations related to disease and treatment. One of the major concerns of brain network application under clinical situations is how to analyze groups of data to find the potential biomarkers that can aid in diagnosis. In this paper, we briefly review common methods to construct brain networks from resting-state fMRI data, including different ways of the node definition and edge calculation. We focus on using a brain atlas to define nodes and estimate edges by static and dynamic functional connectivity. The directed connectivity method is also mentioned. We then discuss the challenges and pitfalls when analyzing groups of brain networks, including functional connectivity alterations, graph theory attributes analysis, and network-based statistics. Finally, we review the clinical application of resting-state fMRI in neurorehabilitation of spinal cord injury patients and stroke patients, the research on the mechanism and early diagnosis of neurodegenerative diseases, such as multiple system atrophy, as well as the research on brain functional network alteration of glioma patients.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Yunxiang Ge and Weibei Dou"},{id:"82099",title:"Recent Advances in the Development of Biofluid-Based Prognostic Biomarkers of Diffuse Axonal Injury",slug:"recent-advances-in-the-development-of-biofluid-based-prognostic-biomarkers-of-diffuse-axonal-injury",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.104933",abstract:"Even though head injury is a silent pandemic of the century producing immense social and economic impact, predictive models have not been established to develop strategies promoting the development of reliable diagnostic tools and effective therapeutics capable of improving the prognosis. Diffuse axonal injury (DAI) is a type of traumatic brain injury (TBI) that results from a blunt injury to the brain. Discovering biomarkers for DAI have been a matter of debate and research. A number of studies have reported biomarkers that are correlated with severity of TBI but no conclusive and reproducible clinical evidence regarding the same has been put forward till now. Additionally, many DAI biomarkers have limitations so that they cannot be generalized for universal applications. The properties of these biomarkers should be extensively researched along with the development of novel biomarkers to aid important clinical decisions for the benefit of the society. This chapter summarizes the existing biofluid-based biomarkers, critically examines their limitations and highlights the possibilities of a few novel biomolecules as prognostic biomarkers of DAI.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Vinu V. Gopal, Rinku Raj Mullasseril and Goutam Chandra"},{id:"81998",title:"Understanding the Neuropathophysiology of Psychiatry Disorder Using Transcranial Magnetic Stimulation",slug:"understanding-the-neuropathophysiology-of-psychiatry-disorder-using-transcranial-magnetic-stimulatio",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.103748",abstract:"Transcranial magnetic stimulation (TMS) is a safe and non-invasive tool that allows researchers to probe and modulate intracortical circuits. The most important aspect of TMS is its ability to directly stimulate the cortical neurons, generating action potentials, without much effect on intervening tissue. This property can be leveraged to provide insight into the pathophysiology of various neuropsychiatric disorders. Using multiple patterns of stimulations (single, paired, or repetitive), different neurophysiological parameters can be elicited. Various TMS protocol helps in understanding the neurobiological basis of disorder and specific behaviors by allowing direct probing of the cortical areas and their interconnected networks. While single-pulse TMS can provide insight into the excitability and integrity of the corticospinal tract, paired-pulse TMS (ppTMS) can provide further insight into cortico-cortical connections and repetitive TMS (rTMS) into cortical mapping and modulating plasticity.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Jitender Jakhar, Manish Sarkar and Nand Kumar"},{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:17,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. 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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. 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She is now a lecturer at the University of Witwatersrand, South Africa, and a principal researcher at the Health Economics and Epidemiology Research Office (HE2RO), South Africa. Dr. Moolla holds a Ph.D. in Psychology with her research being focused on mental health and resilience. In her professional work capacity, her research has further expanded into the fields of early childhood development, mental health, the HIV and TB care cascades, as well as COVID. She is also a UNESCO-trained International Bioethics Facilitator.",institutionString:"University of the Witwatersrand",institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",country:{name:"Spain"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}}]}},subseries:{item:{id:"1",type:"subseries",title:"Oral Health",keywords:"Oral health, Dental care, Diagnosis, Diagnostic imaging, Early diagnosis, Oral cancer, Conservative treatment, Epidemiology, Comprehensive dental care, Complementary therapies, Holistic health",scope:"
\r\n This topic aims to provide a comprehensive overview of the latest trends in Oral Health based on recent scientific evidence. Subjects will include an overview of oral diseases and infections, systemic diseases affecting the oral cavity, prevention, diagnosis, treatment, epidemiology, as well as current clinical recommendations for the management of oral, dental, and periodontal diseases.
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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors"},{id:"17",title:"Metabolism",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation"},{id:"18",title:"Proteomics",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Biochemistry",id:"11"},selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/126657",hash:"",query:{},params:{id:"126657"},fullPath:"/profiles/126657",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()