Threshold element, numbers of points detected, and computational time (the first image of the graffiti sequence, 900 × 640).
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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We rapidly learn to identify, characterize and eventually distinguish those who are near and dear to us. We accept face recognition later as an everyday ability. We realize the complexity of the underlying problem only when we attempt to duplicate this skill in a computer vision system. This book is arranged around a number of clustered themes covering different aspects of face recognition. The first section on Statistical Face Models and Classifiers presents reviews and refinements of some well-known statistical models. The next section presents two articles exploring the use of Infrared imaging techniques and is followed by few articles devoted to refinements of classical methods. New approaches to improve the robustness of face analysis techniques are followed by two articles dealing with real-time challenges in video sequences. 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He continued his studies at TCD and was awarded a Ph.D. in Electronic Engineering for research work in the field of Dielectric Liquids. In 1986 he was appointed to a lectureship in Electronic Engineering at the National University of Ireland Galway. He is currently vice-dean in the College of Engineering & Informatics at NUI, Galway. \r\nHis research interests include embedded systems applications, home networking, digital imaging, pattern recognition, face & fingerprint biometrics, smart grid and wired and wireless networking technologies. He was technical and conference chair of the IEEE International Conference on Consumer Electronics (ICCE) in 2010 and 2011 respectively. 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In 2013 he joined the Centre of Polymer Systems, Tomas Bata University in Zlin, the Czech Republic where was awarded PhD in Chemistry and Material Technology in 2016 defending a research work based on the encapsulation of bioactive compound in biopolymeric matrices. At the moment he is a senior researcher at the Centre of Polymer Systems in Zlin, Czech Republic, in the Bioactive Polymer group and his main attention is focused on developing innovative formulations based on polysaccharides for drug delivery applications.\nIn 2016 he joined the National Research Tomsk Polytechnic University as a Postdoctoral Research in the Department of Technology of Organic Substances and Polymer Materials and his research work mainly focused on the development of hybrid nanocarrier for drug delivery and diagnostic. In 2019 he became associate professor and member of the Research School in Chemistry & Applied Biomedical Sciences at Tomsk Polytechnic University, Russia. He is supervisor and consultant of Master and PhD students and his work is focused on the development of hydrogels for controlled delivery of bioactive compounds for application in pharmaceutical and biomedical area.\nHis primary research interest is the development of nano- and microcarrier based on biopolymer for multiple encapsulations and controlled delivery of bioactive compounds.\nHe has to his credit to 23 publications indexed in Web of Science (h-index 9) and active participation in projects financed by Czech Ministry of education, Russian Ministry of Education and European Union and author of one patent.",institutionString:"Tomas Bata University in Zlin",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Tomsk Polytechnic University",institutionURL:null,country:{name:"Russia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1090",title:"Radiation Oncology",slug:"radiation-oncology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"288104",firstName:"Ivana",lastName:"Spajic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/288104/images/8497_n.jpg",email:"ivana.s@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40586",title:"Polysaccharide-Protein Interactions and Their Relevance in Food Colloids",doi:"10.5772/50561",slug:"polysaccharide-protein-interactions-and-their-relevance-in-food-colloids",body:'Polysaccharides and proteins are natural polymers that are widely used as functional ingredients for various food colloids or emulsion formulations. Majority of food emulsions are constituted with polysaccharide and protein combinations. They are the essential ingredients of any food colloid formulation mainly due to their ability to change product shelf life by varying food texture (Schmidt & Smith, 1992; Schorsch, Jones & Norton 1999). Their interaction in the formulation thus finds many applications particularly in new food formulation development. Due to complex formation and creation of nano or micro structures (aggregation and gelation behavior) they generally change the rheological properties of food colloids which may affect the food product texture and colloidal stability (Benichou, 2002; McClements, 2005, 2006, 2007; Dickinson, 2003). Polysaccharide and protein interactions in solution and interfaces have been studied by several groups (Dickinson, 2003, 2008; Bos &Van Vliet 2001; Carrera & Rodríguez Patino 2005; Krägel, Derkatch, & Miller, 2008; Koupantis, & Kiosseoglou, 2009; Mackie, 2009). However, despite the vast advancement made in the recent past, polysaccharide and protein interactions in food hydrocolloids continue to be one of the most challenging topics to understand.
Proteins, being surface active can play major role in the formation and stabilization of emulsions in the presence of polysaccharide, while interacting through electrostatic or hydrophobic-hydrophobic interactions. On the other hand, polysaccharides being hydrophilic in nature generally remain in aqueous phase thus help in controlling the aqueous phase rheology like thickening, gelling and acting as stabilizing agents. The formation and deformation of polysaccharide-protein complexes and their solubility depend on various factors like charge and nature of biopolymers, pH, ionic strength and temperature of the medium and even the presence of surfactant of the medium (Ghosh & Bandyopadhyay, 2011). If pH of the medium is reduced below isoelctric point (p
Polysaccharides and proteins both contribute to the structural and textural properties of food by changing rheology of food emulsions through their gelling networking system (Dickinson, 1992). Non-covalent interactions between polysaccharide and protein in any emulsion formulation play a major role to change the interfacial behavior and stability of the food colloids. The driving force for these non-covalent interactions is electrostatic interactions, hydrophobic interactions, H-bonding and Van der Waals interactions. Recent literatures also focus on how protein and polysaccharide molecules can be linked together by covalent bond. At pH close to protein p
Polysaccharide and protein complex formation is mainly driven by various non-covalent interactions, like electrostatic, H-bonding, hydrophobic, and steric interactions (Kruif et al 2001). Protein carries +ve or –ve zeta potential based on the pH of the medium (+ve at pH lower than p
Variation of charge density on the polysaccharide and protein chain at various pH ranges.
In general, interactions between proteins and polysaccharides are quite explored where large numbers of report have been published based on the interactions between oppositely charged “protein-polysaccharide” systems (Dmitrochenko et al 1989; Bengoechea et al 2011, Stone & Nickerson 2012). Although electrostatic attraction is the main driving force for the complexation between protein and polysaccharide, but it is also reported that hydrogen bonding and hydrophobic interaction plays a secondary role for stability of the “protein-polysaccharide” aggregates (McClements, 2006). The extent of hydrogen bonding and hydrophobic interaction also depends on temperature (Weinbreck et al, 2004). In 2009 Nickerson and co-workers(Liu, Low, & Nickerson, 2009) have reported that pea protein and gum acacia complex stabilize at low temperature due to increase in hydrogen bonding interactions and destabilize at high temperature due to decline in hydrogen bonding interactions. Temperature also plays an important role to decide the protein conformations (folded or unfolded). In 2007, Pal (Mitra, Sinha & Pal, 2007) and coworkers have reported that human serum albumin unfolds at higher temperature and undergoes in reversible refolding conformations upon cooling (below 600 c). Unfolded conformations of protein expose more reactive sites (amino acids) to the solvent phase, thus more chances of interactions (or binding) with polysaccharide. Binding of anionic polysaccharides (pH~pKa) to the cationic proteins (at pH<p
Two bio-polymers can exist either in a single phase systems or in a phase separated systems depending on the nature of bio-polymers, their concentration, and solution conditions. When two bio-polymers carry opposite charge, then either they agglomerates to form soluble complexes (single phase) or insoluble precipitates (2-phase system). On the other hand, when two non-interacting bio-polymers mixed together, either they exist in a single phase system (where two separate entities distributes uniformly throughout the medium) or exist as two distinct phases (each phases comprise different bio-polymer). Therefore, in the protein-polysaccharide system, phase separation occurs through two different mechanisms which are
Interaction between polysaccharide and protein at various pH.
Schematic representation of the possible mode of interaction between polysaccharides and proteins.
Polysaccharide-protein complexes exhibit a wide range of interesting properties, such as surface activity to stabilize air-water or oil-water interfaces, viscosifying, and gelling properties etc. Viscosifying and gelling ability of polysaccharide-protein complexes help to obtain gel-like processed food products without any thermal treatment in the process. The interfacial properties of these complexes help to impart stability into the emulsion food products. Also, protein–polysaccharide complexes are able to encapsulate several active ingredients; hence they act as delivery systems for many bioactives or sensitive molecules in food formulations. These complexes are also known to vary bulk/interfacial structures, textures and shelf-life stability of the food colloids. In the following section we will discuss this polysaccharide-protein interaction in light of their functional properties.
Viscosity and gelling are the rheological property which depends on the molecular characteristics of the biopolymers, such as their molecular weight, shape, chain flexibility. Other factors are the concentration, interaction between the biopolymers and water, as well as solution parameters like: pH, ionic strength and presence of other components/ligands etc. Interactions between polysaccharide and protein have been proven to widen the functional properties of each individual biopolymer. Rheological properties of polysaccharide-protein complex lead to new rheological behaviors different from each individual biopolymer.
Association of two bio-polymers is expected to increase the bulk viscosity of the system as entities of larger sizes are formed. The rheological behavior of several protein–polysaccharide mixed systems have been studied and ranged from viscous to viscoelastic properties showing elastic behavior is reported. The hydrated polysaccharide-protein complexes increase viscosity and rheology of the system found to be depends on the nature and structure of polysaccharides. Viscous property of gum acacia-protein coacervates attributes to the globular conformation of the polysaccharide, whereas the same protein with linear pectin results in gel-like system. Beside the nature of the individual bio-polymer solution property and concentration of bio-polymers are also known to affect the rheology of the system (Dickinson, 2011). For example, it was found that pH played a major role in the viscosity of the coacervate phase. A maximum viscosity was obtained at pH 4.0, where concentration of whey protein and gum arabic in the coacervate phase was maximum and extent of electrostatic attraction was highest. This suggests that the electrostatic interactions between whey protein and gum arabic were responsible for the highly viscous behavior of the coacervates. Whereas, the same composition of whey protein and gum arabic at pH above protein p
Viscoelastic properties of polysaccharide-protein complexes also play an important role towards the foam stability in variety of food products. In case of air-water system foam can be define as the air entrapment by a thin liquid film (water), where this liquid film is stabilized by some surface active molecules. Stability of the foam increases with the increase in the stability of the interfacial liquid film, because lower stability of this interfacial liquid film can lead to the diffusion of air entrapped inside the foam. Viscosity of this liquid film is another parameter by which one can control the diffusion rate of air entrapped inside the foam. Therefore, higher stability and viscosity of the interfacial liquid film leads to lower diffusion of air entrapped inside the foam and increase the foam stability. Schmitt and co-workers have studied the air-water interfacial property of β-lactoglobulin-acacia gum complexes at pH 4.2 [Schmitt et al 2005]. The group has reported that although surface activity of the complex is similar with the pure protein, but complex forms much stronger viscoelastic interfacial film with thickness of about 250 Å. As a result, gas permeability of thin-film stabilized by the complexes was significantly reduced (0.021 cm s -1) compared to pure β-lactoglobulin (0.521 cm s-1). This phenomenon suggests that stability of foam (stabilized by protein-polysaccharide complex) is higher compared to the foam stabilized by protein alone.
The likely explanation of the higher foam stability and different interfacial properties of coacervate is that protein-polysccharide complexes are able to re-organize at the interface by coalescence, forming interfacial microgel. These findings were applied for the ice cream formulation for improved air bubble stability (Schmitt C, Kolodziejczyk E. 2010). Similarly, gelatin has been replaced by whey protein isolate-gum acacia complexes to improve the bubble stability in chilled dairy products (Schmitt C, Kolodziejczyk E. 2010). In case of stabilization by complexes, variation in ratio of biopolymers could be used to control the size of the complexes, hence their surface activity. In addition to that, viscoelastic properties of the air-water interfacial film is possible to tune by either adsorbing two biopolymers simultaneously or by the sequential adsorption of protein followed by polysaccharide. As for example, β-lactoglobulin-pectin complexes are known to stabilize the air-water interface. In this case, thickness of the film obtained from the sequential adsorption of protein and polysaccharide was higher (450 Å) than the adsorption of complexes (250 Å) (Ganzelves et al 2008).
In contrary to air-water foam stability, use of polysaccharide-protein complexes for the stabilization of oil-water emulsion (Martínez et al 2007) has received much more attention. Use of these polysaccharide-protein hydrocolloids as an emulsion stabilizer will be discussed in the next section.
Emulsion is a uniform dispersion of liquid droplets within a continuous matrix of a second immiscible liquid, stabilized by surface active molecules. These stabilizers are termed as emulsifier. In the context of the present topic, we will limit our discussion to the role of bio-polymers as emulsifier. Generally, emulsifier has the amphiphilic character to adsorb onto the interface of liquid droplets, which can prevent the phase separation of two immiscible liquids. For a fixed emulsifier, stability of the emulsion depends on few factors, such as rate of adsorption of the emulsifier, concentration of emulsifier, etc. At low concentration of emulsifiers, emulsion system fails to retain its initial droplet size. This destabilization can take place through different mechanisms. In case of poor coverage of the interface by liquid droplets, they can coalesce with each other to form a bigger droplet (Fig. 4). Few examples are also reported, where polymer adsorbed onto the interface of liquid droplets thus bridge between two such liquid droplets and initiates bridging flocculation. Interestingly, emulsions at high emulsifier concentration produces stable oil droplets due to better coverage of the interfaces of the liquid droplets ( Liu & Zhao, 2011).
Emulsion is possible to achieve by using many surface active agents, such as small surfactant molecules, bio-polymers (proteins or polysaccharides, hydrocolloids (protein-polysaccharide complexes), and inorganic particles. Stability of those emulsion systems mainly governs by the two important factors. First, repulsive force between two closely approaching liquid droplets; second, Ostwald ripening, which involves disappearing of smaller droplets in expense of the formation of larger one. Higher degree of repulsion between the two neighboring droplets results in maximum stability due to least chances of coalescence. Repulsive force between the two liquid droplets govern by the inter droplet distance, i.e. thickness of the thin liquid film between two closely approaching droplets. Thickness of this liquid film depends on the space occupied by the adsorbed molecules (emulsifier) at interface of the droplets. Emulsion generally get stabilized by different emulsifiers present in the formulations such as surfactants, proteins, or hydro-colloids (protein-polysaccharide complexes) and the relative thickness of the liquid film between two closely spaced droplets lies in the order of hydrocolloids (5-10 nm) > proteins (1-5 nm) > surfactants (0.5-1 nm) (Fig. 5). Therefore, stability of the emulsion droplets expected to be higher when they are stabilized by protein-polysaccharide complex compared to the same stabilized by protein or surfactant molecules. In addition to the thickness of the liquid film between two closely spaced droplets, rate of desorption of the emulsifiers from the interface is another important factor. Adsorption of emulsifier molecules (like surfactants, proteins etc.) at the liquid interface is highly reversible. Desorption of the emulsifiers from the liquid interface governs the instability of the system. According to this fact, emulsion stabilized by particles (size ranges from 10 nm to several μm) is likely to have indefinite stability, because of the maximum thickness of thin liquid film in between two closely spaced droplets and maximum desorption energy of the particles from the liquid interface. Despite of this theoretical consideration, experimental evidence by Tcholakova et al. does not support this hypothesis that particle stabilized emulsion are more stable compared to surfactant or protein or hydrocolloids stabilized emulsion (Tcholakova et al. 2008).
Schematic representation of mode of stabilization and destabilization of oil droplets in an oil-water emulsion.
Schematic representation of the relative thickness of the thin liquid film between two closely spaced droplets, stabilized by A) surfactant, B) protein, and C) protein-polysaccharide hydrocolloids.
Another factor guides the emulsion instability is Ostwald ripening, which is disappearance of small size droplets at the expense of the larger droplets formation. Driving force for the Ostwald ripening is the difference in the chemical potential of the smaller and larger droplets. Mass transfer takes place between the droplets by diffusion process. Therefore, Ostwald ripening process requires the solubility of the dispersed phase into the continuous phase to initiate the diffusion process. Type of the emulsifier also plays an important role towards the Ostwald ripening process. Emulsion stabilized by the water soluble surfactant molecules has lower interfacial tension, which reduces the thermodynamic driving force of Ostwald ripening. Adsorption of the surfactant molecules at the droplet interface is a reversible phenomenon. Reversible desorption and adsorption of the surfactant molecules from the interface of the liquid droplet increases the rate of mass transfer between the dispersed droplets, hence increase the Ostwald ripening. The chances of desorption of emulsifier is less in case of the emulsions stabilized by protein molecule because it provides a thicker layer (elastic layer) around the droplets and greater surface coverage of the interfacial area. These factors reduce the ripening process in the protein stabilized emulsion. Ostwald ripening process is possible to avoid completely, only if emulsion is stabilized by insoluble particles (due to very high desorption energy) or thickness of the elastic layer around the dispersed droplets is equal to the droplet radius (Kabalnov, 2001). For this reason particulate emulsions are able to prevent the ripening process completely. Whereas hydrocolloid (protein-polysaccharide complexes) mostly behaves like a soft polymer, more resembles with the protein structures compared to solid particles, which cannot completely avoid the ripening process.
Protein–polysaccharide complex stabilized emulsions are possible to obtain by using two alternative ways. One of them involves addition of charged polysaccharide solution to a primary emulsion which is already stabilized by the protein as single emulsifier, to produce emulsion droplets having a protein–polysaccharide ‘bilayer’ surface coating (Fig. 6B). Another method involves addition of an aqueous solution containing the protein–polysaccharide complexes as an emulsifying agent following homogenization (Fig. 6A). For convenience, the first method is termed as ‘bilayer emulsions’ and second one is termed as ‘mixed emulsions’. The bilayer approach is also commonly known as ‘layer-by-layer’ approach. Recently, it has attracted significant importance because of its use in nano-encapsulation and protection of emulsions against severe environmental stresses. The major problem lies in ‘layer-by-layer’ approach, where emulsion droplet tends to flocculate. Flocculation during the ‘layer-by-layer’ adsorption takes place because of two different mechanisms: a) bridging flocculation, b) depletion flocculation.
Schematic illustration of two alternative approach of preparing oil-water emulsion using protein-polysaccharide complexes as an emulsifier. A) Mixed emulsions, both protein and polysaccharide present together during emulsification. B) Bilayer emulsion, polysaccharide added to emulsion prior stabilized by protein.
Generally, encapsulation includes all aspects of protection or stabilization of active molecules (flavors and bio-actives) against several external drastic conditions (such as heat, redox potential, shear, temperature, light, oxygen, moisture, etc.). Controlled release facilitates the delivery of the encapsulated material to the targeted place with the optimal kinetics. Conditions for the encapsulation of active molecules depends on the sensitivity (thermal and redox stability) and nature (solubility in oil and water) of the active components but release can be controlled by mechanical process, pH variations (acidic conditions in the stomach, neutral in the intestine) or enzymatic actions etc. As for example, Peniche et al. has shown the encapsulation of shark liver oil (rich in poly unsaturated fatty acids) using chitosan-alginate system to mask the unpleasant taste of oil. These capsules disrupts by enzymes, like lipase or pancreatine. Initially it was resistant to the acid environment of the stomach, but after 4 hour in the intestinal pH (pH 7.4), the capsule walls weakened and delivers the active molecules.
Another important application of this aspect is the encapsulation of flavor molecules. Recently, Yeo et al. has shown that gelatin–acacia gum coacervate can encapsulate flavors which can be released during cooking in baked goods (Yeo et al. 2005). Weinbreck et al. (Weinbreck et al. 2004) has shown that Whey protein–acacia gum coacervates can encapsulate lemon and orange flavors and their release under mechanical action like chewing. Encapsulation was one of the first applications of gum arabic-gelatin coacervates (Bungenberg de Jong and Kruyt, 1929). Viscous coacervate was made at a temperature higher than the gel point of gelatin and during cooling, interfacial rigidity increases which lead to a stable gelled shell around the microcapsule. This rigid shell disrupts after consumption, gelatin melts easily in the mouth and therefore releases the encapsulated actives. In addition to gelatin-acacia gum, several other protein-polysaccharide systems have been evolved (whey proteins, plant proteins, pectin, and xanthan gum) to broaden the encapsulation techniques. Beside these polysaccharide-protein combinations, process parameter for encapsulation (pH, ionic strength, macromolecular ratio, and drying/homogenization procedure) also plays an important role to modulate the physical properties (thickness, swelling rate, etc.) of the coacervate layer in the microcapsules (Savary et al 2010). Use of cross-linking agents can further harden the coacervate layer after formation of the microcapsules. As for example, use of trans-glutaminase can introduce covalent linkages between carboxyl group of a glutamine and amino group of lysine in the protein molecules. Alternatively, formaldehydes and glutaraldehydes have also been studied although they are non-food grade reagents. Recently, tannic acid, plant phenolics, citral molecules and glycerin have been studied as food grade alternatives (McClements, 2010).
Contrary to the encapsulation through coacervation, the bi-layer emulsion technique (formed by successive adsorption of biopolymers at the interface) is another way to study the microcapsule properties. Recently, McClements group has described that bi-layer approach of encapsulation has a better control of the interface structure, charge, thickness and permeability with improved stability and controlled release of actives. Group has reviewed this research area and discussed multilayer emulsions in light of bioavailability control and release of actives to the specific site of action depending on layer composition and properties (McClements, 2011). Sagis et al. has used high molecular weight pectins and pre-heated whey proteins (denaturation of protein) for the encapsulation through multilayer approach. (Sagis et al 2011).
Basic understanding of supramolecular chemistry which span among origin and nature of the various non-covalent molecular interactions between polysaccharides and proteins can be widely used to create various desirable nano and macro structures which are quite significant in food colloids/formulations. Food product texture modulation and colloidal stability can be achieved by controlling protein-polysaccharide interactions. Modulation of this interaction by varying medium conditions like pH, ionic strength etc. one can create many possibilities towards rheological properties of food colloids which may affect the emulsion stability. Protein-polysaccharide interactions are well characterized in various pH conditions. Although, the interaction depends on type and nature of biopolymers, no structure-activity correlation has been established until now. Moving forward, there is a huge demand to establish a correlation between biopolymer structures and interaction efficiency. The creative manipulation of polysaccharide-protein interactions can open up a completely new dimension in health and nutrition platform. Food particle travels from mouth to gut in various pH environments (for example pH decreases when it moves from mouth saliva to stomach and increases when partially digested food particle passes from stomach to small intestine), thus one can design a smart polysaccharide-protein complex system which can encapsulate or slow down and trigger or release of nutrients in various stages of digestion process depending on the pH of the system and any particular health demand. Such kind of pH sensitive system design and product development which works in vivo is a real challenge for the food scientists. Unfortunately, general understanding of protein-polysaccharide interactions, their bulk and interfacial properties toward emulsion stability is not completely understood and requires more systematic investigation in future to unveil its full potential.
Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been made by advances in multi-disciplinary areas of research related to neuroscience, brain-imaging techniques and human-computer interfaces. The end goal of a BCI is to enable monitoring of the underlying brain processes and subsequent utilization of this information for communicating and controlling devices solely through the brain without depending on the normal output pathways of peripheral nerves and muscles. Photographs capture reality. However, this belief no longer holds true in the current digital era given that the manufacture of counterfeit images has increased [1]. The development of powerful photo editing software, such as Photoshop, has simplified the production of fake digital images [2]. A case of image counterfeiting is shown in Figure 1. Image forgery has severe consequences. For example, by modifying faces in an image, image counterfeiting can be applied to ruin a person’s reputation. Academic documents may also include manipulated images that misrepresent experimental data. In addition, image forgery can be applied to remove a reference object from a standard image. As a result, the validity of the image can no longer be accepted [3]. These multilevel protection issues have different implications in different fields, such as detective work.
Image forgery has severe consequences.
In simple terms, a brain-computer interface (BCI) is a direct interface between the human brain and an artificial system. Its purpose is to control the actuation of a device, say a robotic system or a wheelchair, with brain activity but without the use of peripheral nerves or muscles [4]. BCI in a literal sense means interfacing an individual’s electrophysiological signals with a computer [5]. Thus, in a true sense, the BCI only uses signals from the brain and must consider eye and muscle movements as artifacts1 or noise. Information from various knowledge domains is necessary to create a complete BCI system. Thus, an artificial neural network (ANN) is an information-processing paradigm that is inspired by the way in which biological nervous systems, such as the brain, process information. This network is composed of a large number of highly interconnected processing elements referred to as neurons that work in unison to solve specific problems. Enhancing the noisy electroencephalogram (EEG) signal utilizes a layer of neurons in the spatial dimension within the neural network framework. The incoming noisy input signal sample is treated as a probability density function (pdf) by the layer of neurons and it recurrently evolves under the influence of the SWE and appropriate learning rules. This approach has made possible the development of an efficient computational algorithm referred to as the recurrent quantum neural network algorithm (RQNN) which to some extent has solved the complex problem under consideration. In general, two methods can be applied to detect image fraud: active and passive certification [6]. These two methods are illustrated in Figure 2. Active certification is categorized into two classes. The first class is based on the identification of a digital watermark. A watermark is hidden in the image at the end of capture, the detection program checks if the image certificate has been edited [7, 8]. The watermark is inserted when the image is taken using a specially equipped photographic camera or after acquisition by an expert [1]. The successive editing of the original image may degrade image quality. Passive certification methods are based on digital signatures. These methods identify the distinguishing characteristics of an image as a signature after image acquisition. At the end of certification, signatures are renewed in accordance with a similar method, and the genuineness of the image can be identified by comparison. Digital signatures and watermarks have similar disadvantages. Negative image certification, also referred to as forensic digital image certification, is highly practical. Digital image certification does not require extra information and is independent of the image theme [9]. Negative methods have two parts: (1) identification of the original edit and (2) detection of tampering [10]. Certification for the first class is based on digital fingerprint certification, effects allowed by image acquisition, and storage. The methods used in this class use the digital fingerprint of the camera to differentiate among similar or dissimilar camera models. The detection methods of passive falsification can either be false or independent. Fraud detection methods are employed in particular cases of counterfeiting, similar to making copies or linking images. To discover universal forgery, researchers use autonomous techniques and exploit three different types of artifacts: the effects of resampling, pressure, and contradictions [10]. The types of counterfeiting techniques can be categorized into two classes: copy-detecting technique (image forging) and image-binding technique (two-fold image-based counterfeiting).
Detect image fraud: active and passive certification.
The ease and effectiveness of counterfeiting facilitates its application in changing image content [11]. The important features, like the pallet and the active range, of replicated areas are compatible with the rest of the image given that these areas are obtained from the same image [12]. Nevertheless, in practice, counterfeiting may imply more than simple replication. Numerous image-editing processes may be applied in serious counterfeiting, as shown in Figure 3. The processes can be divided into two groups: intermediator processes and postprocesses. Intermediator processes are applied to synchronicity and homogeneity between a replicated region and its neighbor [13]. Intermediator processes include rotation, scaling, reflection, lighting adjustment, or color adjustment. In serious cases, intermediator processes can be combined. Postprocesses, such as noise addition, joint photographic expert group (JPEG) compression, or blurring, can be applied to delete all retraces that can be detected in the copy process, such as sharp edges [2]. A broad range of easily available algorithms has been proposed to detect replicated images and functions, as shown in Figure 4.
Image processing operations associated with image forgery.
Pipeline of a fraud detection algorithm.
To detect image forgery, an image is first selected (e.g., converted to gray scale). The image is divided into an auction block of nested pixels. The size of the image
The vector is an extractable characteristic in each block. The vector-matching function is highly similar to pairing functions. Known pairing methods include the arrangement of miracle dictionaries on the element vectors and the identification of the nearest neighbor in the tree
Numerous articles on the negative detection of displacement in images have been and continue to be published. Existing methods for displacement detection are primarily distinguished on the basis of the case and sizing of the function applied to match the image block. This article classifies existing methods in accordance with the extracted properties applied to test block similarities. In the following sections, different cases or classes of detection algorithms are presented.
In contrast to other algorithms, this algorithm does not divide the image into auction blocks to extract features but instead extracts features from the intact image. Feature extraction is performed with SIFT and speeded-up robust feature (SURF). This technique is applied to derive the characteristic local feature of an image and produce a keypoint in accordance with preset requirements. The vector sum/descry values are fixed for rotational, translational, and scale measurements and are partially fixed for strong illumination changes in local geometric distortion [14, 15]. The first attempt to exploit this algorithm was reported by [16]. In the algorithms, only the correspondence of the keypoint can be achieved by its maximum bin, including the identity of the nearest neighbor [17]. SIFT has been adopted to identify replicated regions in a counterfeit image. The SIFT signifier is applied to detect copied areas by coping with keypoints rather than clusters. This algorithm has excellent detection accuracy but otherwise poor performance.
He proposed the SIFT algorithm, which could be used to detect and evaluate the geometrical shifts applied to forged displacement copy-and-paste images. The detection procedure involves three steps: In the first step, SIFT functions are extracted and main points are associated. The second step is committed to keypoint compilation and fraud detection. The third step estimates the engineering shifts, if any, that occurred. SIFT can be executed under the conditions of eminent real rate (TPR) and abject fake positive degree ratio (FRE), JPEG compression, and additional noise. In addition, SIFT can accurately estimate different arguments for affine transmutation. Figure 5 shows different arguments for affine transmutation.
Different arguments for affine transmutation.
The first attempts to take advantage of SIFT have been reported in [16]. In SIFT, the correspondence of the key indicator is achieved by first identifying the neighbor closest to the best bin [17]. SIFT has been adopted to identify a single copy in the counterfeit image. SIFT descriptors are usually applied to identify keypoints of copied areas instead of blocks, whereas other algorithms cope with object indicators. Although SIFT exhibits excellent detection performance, its false–positive rate remains unknown. In [18], the main SIFT points were extracted from the image and were then associated to obtain the corresponding keypoints. A vote scheme based on vector direction was applied to distinguish between origin and direction. Then, an efficient two-fold sub-window search algorithm (EES) was used to locate duplicated areas within the border box. Finally, a pixelwise partition was identified. The experiment solutions demonstrated that the proposed algorithm remains robust even with background noise and engineering manipulation [19]. He suggested a SIFT algorithm that could detect and then estimate the geometrical transformation applied to forge displacement copy-and-paste images. The detection process involves three steps. In the first step, the SIFT function is extracted and corresponding keypoints are identified. The second step involves the consolidation and detection of fraud. The third step identifies changes that occurred. SIFT has high positive identification rate and low false positive rate even under JPEG image compression and added noise conditions. In addition, it accurately estimates several affine transformation parameters. Refs. [20, 21] suggested a SIFT-established detecting algorithm that can be used to estimate the geometrical transformation applied to the copy. The algorithm begins by converting the suspected image into grayscale. SIFT is then applied to collect image characteristics for the detection of keypoint sources. In SIFT, the keypoint sources are initially adapted in accordance with the characteristics of the vector sum used in the better bin-first algorithms. The potential geomagnetic distortion of the refined areas is estimated on the basis of the assumed paired keypoints by applying RANSACK. SIFT is more robust than intermediary processes even when JPEG compression or noise are added to the processed image. Furthermore, affine transformation is exactly estimated, particularly in larger duplicated areas. A different scenario is to integrate SIFT into copy detection systems [22]. Instead of applying SIFT to detect keypoints, the Harris quicker from SIFT is applied. After all keypoints are revealed, SIFT is applied to generate the descriptive characteristics of extracted features. Then, the
SURF has been adopted to detect image editing processes, such as rotation and gradation. SURF is superior to SIFT in detecting image strengths and performs as well as SIFT. The complicated automatic reinstallation of duplicate areas hinders the practical applications of these algorithms. We propose a novel algorithm for the detection and description of scale and constant rotation in images. The algorithm is based on SURF and thus has powerful acceleration functions. SURF approximates or even exceeds the proposed thresholds for redundancy, excellence, and sustainability and rapidly performs calculation and comparison. This operation is performed by relying on image confluence. The exit detection and prescriptive prescriptions are based on their strengths (if a Hessian scale is used to detect and describe the established distribution), and kernel methods are simplified to allow the combination of new detection, description, and correspondence. Correspondence between two images of the same view and the objective is partly achieved by using many computers. In this study, photography, three-dimensional reconstruction, image recording, and objective recoding were conducted. The search for a separate image match—the purpose of our research—can be separated into three principal steps. First, points of interest are specified in the characteristic locations of the image, such as angles, points, and plus T-intersections. The most important property of a detection method is its repeatability, that is, its reliability in finding similar indicators of interest under different conditions. Then, each point of interest is represented by a transmitter characteristic. This description must be distinct and must have similar time strengths under noise conditions, mistake detection, and geometrical and photometrical distortions. Finally, vector descriptors are adapted in different images. Correspondence is based on vector distance. Descriptor size directly affects computational time. Thus, fewer dimensions are desired. We aimed to develop an algorithm for the detection and the identification of fraud. We compared the performance of our proposed algorithm with that of a state-of-the-art detection algorithm. Our algorithm exhibits computational time and robust performance. Downsizing after description and complexity must be balanced while providing sufficient distinction. Various detection and description algorithms have been proposed in the literature (e.g., [1, 2, 3, 6, 7, 23]). Furthermore, detailed datasets for comparison and standard assessment have been established [8, 9, 10]. We build upon the knowledge gained from previous work to better understand the aspects that contribute to algorithm performance. When used in experiments on standard image sets, as well in the application of actual objective recognition, the algorithm exhibited rapid detection and description, as well as distinctive and reproducible performance. While working with local features, stability is the first issue that requires resolution and depends on the expectation of geometrical and photometrical distortions. This turn of events is identified by the possibility changing in conditioning. We concentrate on the detectors and constant descriptions of the balance and rotation of the image. These detectors offer better compromises among the complexity of the functionality and the durability of the distortions that usually occur. The discrepancy and gradient of anomalies and the effects of perspective are secondary to the effect covered by the overall durability of the description [2]. The additional complexity of affine invariance negatively affects sustainability, unless significant changes are anticipated. In some cases, even analog rotation can be abandoned with solutions in a fixed static version of our description. We refer to this ability as “erect SURF” (U-SURF). In fact, in some applications, such as cell robotic navigation or visual guidance, the camera often only revolves around the vertex. Taking advantage of avoidance of the exaggerated stability of rotation in similar events not only increases speed but also increases discriminatory force. As for the photometric, we assumed a simple linear accelerator example with a scaled factor and displacement. Note that our detection and description do not apply color.
The most commonly used detection method is the Harris-cornered method [24], which was proposed in 1988. It is based on the intrinsic values of the secondary-momentary matrix. However, Harris angles are not fixed. The Lindberg detection method introduces the principle of automatic scaled selection [1], which allows the detection of a full point of interest in an image together with its scope. He experimented with the Hessian matrix operation identifier and Laplacian (corresponding to the Hessian matrix operation effect) to detect bulb structure. The detectors, which were developed by Harris-Laplace and Hessian-Laplace, are robust and stable with high reproducibility [25]. The Harris (Adaptive Scale) or the Hessian Matrix Locator and Laplacian have been applied to determine scale. Focusing on speed, [26] estimates Laplace Gaussians (LoG) on the basis of the candidate Gauss (DoG). Several fixed-interest rate detectors that increase the entropy in the area and the edged-based zone detection have been proposed [11]. Nevertheless, these detectors are inflexible. Several detection methods have been proposed for fixed properties that can adapt to long-term changes but are not discussed in this article. A review of the literature [9, 12] shows that (1) Harris-based detection methods are stable and replicable. The use of a specific Hessian matrix addition instead of its effect (the Laplacians) is useful because fires occur less on elongated and nonlocal structures. In addition, (2) an approximation, such as DoG, has low-cost computational speed and low loss of precision. A wider set of attribute descriptions has been suggested, such as the Gaussian-derived function [13], a fixed moment [27], complex feature [4, 28], guiding filters [29], and phase-localized functions [30], to represent the distribution of small features in a region of interest. The latter [2] has been shown to surpass the others [8] because they capture a basic quantity of information on the special intensity of level models when large to small deformations or localization mistakes occur. In [2], SIFT has been applied as a general level gradient diagram around the indicator of interest and is stored in boxes in a 128-dimension vector (eight routing boxes for each 4 × 4 box). Various improvements have been proposed on this basic scheme [3]. PCA has been applied to slope images. These operations (PCA, SIFT) provide a 36-dimension characteristic that is rapidly harmonized but is less distinct from SIFT in terms of secondary comparison [9]. The slow calculation function reduces the impact of quick coping. In similar papers [9], the authors suggested a variation on SIFT, named GLOH, which proved to be more distinct with the same dimensional count. However, GLOH is computationally expensive. SIFT is the most attractive for practical application and is currently the most widely applied algorithm. It is distinct and relatively quick, which is crucial for online applications. Recently, [31] used a field-programmable area grid to improve its order of magnitude relation. However, the height dimensions of the descriptions in SIFT are defective when compared with those of corresponding methods. For online applications on an ordinary computer, each of the three steps (detection, description, and correspondence) must be fast. Alternatively, best-bin-first [2] accelerates computation but provides inaccurate solutions. A novel detection method based on SURF has been proposed by [1, 25]. However, basic approximation was applied because DoG [2] is a basic Laplacian-based detector. Given that it depends on the embedded image to reduce computing time, we designated this algorithm as the “Quick Hessian” detector. Description, on the other hand, describes the distribution of the Haar-wavelength reactions in the area of interest. We operate the built-in speed images repeatedly. In addition, only 64 dimensions are used, thus decreasing the calculation time of the corresponding characteristic and simultaneously increasing durability. We again propose a new index step based on the Laplacian marker. This step accelerates correspondence and increases the robustness of the description. To illustrate the self-sufficiency of the algorithm, we briefly discussed the conception of an integrated image, as defined in [32]. It allows the quick execution of filter to wrap a box type. The insertion of an integrated image IΣ (z) into
The calculated IΣ only requires four additions to calculate the total intensity on any vertical and rectangular surface, regardless of its shape.
We based our detection method on Hessian matrix addition because of its superior calculation time and accuracy. Therefore, instead of using an applied range to select position and scale (as in the Hessian-Laplace [25]), we used a Hessian identifier for both. Given the indicators
where, similar to L,zy(z, σ) and L,yy(z, σ), L,zz(z, σ) is the rotation of the Gaussian second-order differential ∂2 ∂,z2 g(σ) with the image I in indicator
Left to right: (intact and trimmed) Gaussian secondary arrangement partly derived in the
When applied to rectangular areas, SURF remains simple and arithmetically efficient. However, we need additional relative weight in equilibrium. This weight is specifically expressed with
where |z|F is the Frobenius norm.
In addition, the responses to the filters are normalized to mask size to ensure that the continuous Frobenius is standard for any filter size. In an image, space generally takes the form of a triangle. The image is repeated with a Gaussian filter and subsamples to reach the apex of the triangle. Given the application of box filter and plot image, we do not duplicate the filtering to output a previous filter layer. Nevertheless, filters of any size can be used at the same speeds when applied to the original image (even parallel to the latitude, if not used here). Therefore, size spacing is analyzed by increasing filter size rather than decreasing image size. The output of the 9 × 9 filters above is considered as the primary gauge level. Thus, scaling
This view clearly shows the Hessian-detecting characteristics. Medium: Warp types applied in SURF. Right: Image of graffiti showing the size of the window descriptors on different scales.
The first levels of each Octavian are relatively large. Figure 7 (left) shows the points of interest detected when quick-Hessian detection is applied.
Left: points of interest detected in an image of a sunflower field.
The superior performance of SIFT compared with that of other [9] benchmarks is remarkable. Their mixing with local informatics and the distribution of gradient-related characteristics provide fine characteristic resistance that mitigates the effect of settlement faults in terms of size or surface area. The application of relative resistance and gradient directions decreases the effect of illumination changes. The proposed SURF descriptor is based on similar properties, further complicating the process. The first step is to identify a direction that can be reproduced from data from a circular area surrounding the indicator of interest. Next, we construct a square area aligned with a specific orientation and extract the description from it. In addition, we also offer a vertical version number of our descriptor (U-SURF), which is not fixed for in image rotation and rapidly calculates and improves camera location.
To fix the rotation, we define a reproducibility orientation for points of interest. To this end, we first compute the Haars wavefunction that corresponds to the
To extract a description, a window centered around the indicator of interest must be constructed. The area must be oriented in the direction specified in the previous section. This transformation is unnecessary for a vertical copy. The size of this window is 20 s. The area is regularly divided into small 4 × 4 subregions to preserve crucial data in each subregion. We calculate some simple characteristics in a 5 × 5 regularized subregion. For simplification, we designated the DEX response waveform Haars in the horizontal direction and colored the prepared Haars corresponding to the vertical angle direction (2S filters size). Here, the terms “horizontal” and “vertical” are defined with respect to the orientation of the specified point of interest. To increase robustness to geometrical distortions and localization faults, the DEX and dy responses are first weighted with a Gaussian (σ = 3.4 s) centered around the indicator of interest. Then, the wavelength and dz. and dy wavelet responses are summarized above each subregion and are the first place of inputs in the vectorial function. To provide data on changes in polarity density, we also extract total absolute value for the replay of |dz| and |dy|. Thus, each sub region has a four-dimensional descriptor for the underlying intentional structure that leads to a vectorial description of all 4 × 4 sub regions of distance 64. Wavelength response is constant to polarize the illuminated “offset.” Contrast (factor range) is obtained by converting the description into a vector unit. The characteristics of three different image intensities in a subregion. Imaging groups of these general density models can be applied to produce a distinct description. To access the SURF descriptor, we experimented by subtracting and adding waves, applying d2z and d2y, adding first-order waves, applying PCA, and identifying the intermediate and average values. From a comprehensive evaluation, the outer part performs best among all parts (Figure 8).
Descriptive entries for a subregion representing the universal base density model.
Left: the state of a homogeneous zone. All values are relatively small. Center: in the presence of frequency in the direction of
We change the sampling count for indicators and subfields. A sampling subregion of 4 × 4 provides good results. Given the fine divisions, it appears to be less powerful, significantly increasing the timing of correspondence. In other methods, the shortage circuit with 3 × 3 subregions (SURV-35) provides poor results but allows for rapid computation e and is relatively acceptable compared with other descriptors in the literature. Figure 9 shows just some of the compared results (SURV-126 will be explained soon).
Line graphs for different methods.
The two different match strategy tests performed on the “Graffiti” image with width changes of 30 points from the current description. Points of interest are calculated through the “Quick Hessian” detection method. Note that rates are unfixed per affine. Therefore, the results are not identical to those of [9]. Surf-126 corresponds to the expanded description. Left: similarity between threshold element and match strategy. Right: strategy for closer contact.
We test another section of the SURF descriptor by adding two similar characteristics (SURV-126). It repeatedly uses the same quantities as before but has additional divisors. The values of dz and |dz| are calculated individually for dy < 0 and dy ≥ 0. Likewise, the values of dy and |dy| are separate and agree with the signal of dz, thereby duplicating the count of the feature. Description is more distinct and does not require long computation time. However, matching time is slow because of the high dimensions of the features. The argument choice is equated for the “Graffiti” sequence [9] because it contains out-of-play rotation in the rotation map, as well illumination changes. The general description of 4 × 4 sub regions (SURF-126) improves performance. In addition, SURF has excellent performance that surpasses that of the latest state-of-the-art algorithm. To provide an index of the pairing phase, Laplacian signs (i.e., the Hessian matrix effect) are included for the basic point of interest. Typically, the points of interest are in plug-type structures. The label marks luminous points on the darker background of the reversed situation. This functionality is available at an additional price, which has already been calculated throughout the detecting process. During matching, we compare the feature only if they have the similar contrast types. Thus, this minimum data speeds up matching and improves performance.
We provide solutions for a standard evaluation set without detection and description. Then, we discuss the solutions obtained during when applying the algorithm to apply the real object. All detectors and descriptions are based on comparison with the original application of the authorizer. In standard evaluation, we test our detectors and describe the applied sequence of images and software tests. The test set included images of actual, narrow, and structured scenes. Given the limited page count of this manuscript, we cannot provide the results of all sequences. To compare the performances of the detectors, we selected images with changes in perspective (Graffiti and Wall), magnification and rotation (Boats), and illumination (Leuven). Test notes for all sequences are presented in addition to the base sequence. We applied the degree of repetition, as described in [10], to detect the number of points of interest in two images relative to the indicator of interest (which is only the visible part of both images). The performance of the detection algorithm was compared with that of the Gaussian (DOG) [2], Harris, and Hessian Laplace [12] algorithms. All algorithms provided similar number of points of interest. This finding applies to all images, including the database used in the object recognition experiment (see Table 1 for an example). In addition, the computational speed of our Quick Hessian detector was more than three times faster than that of DOG and five times quicker than that of Hessian Laplace. At certain timepoints, the repetitions of our detector approximated (Graffiti, Leuven, Boat) or exceeded (Walls) that of the competition. The Graffiti and Walls sequences contained out-of-play gyration, and solutions in affine contortions when the detection compared only gyration and were scaled invariantly. Therefore, distortions must be addressed through the overall durability of features. The descriptors were evaluated by the applied call diagrams (1 precisely) in [3, 9]. In each evaluation, we applied the first and fourth images of the sequence, except for the Graffiti image and the Walls scenario. The corresponding perspective change was 30 and 50 points., we compared our SURF signifier (GLOH0, SIFT, and PCA-SIFT) with our “Quick Hessian” detector. SURF outperformed the other signifiers in almost all tests. In Figure 4, we equated the solutions applied to two different corresponding techniques—one established on the same threshold element and one founded on the closest neighbor proportion (see [9] for a discussion of this technique). This phenomenon affected the order of descriptors but SURF performance is better in both events because of limited spacing. However, the only solutions on likeness similar to the similarity threshold are shown in Figure 7 because this technique is most appropriate for representing the runner distribution in its advantage spacing [9] and used more routinely. SURF descriptor is systematically and extensively superior to other descriptors and exhibited 11% improvement. Its computational time is rapid (Table 2). The microprocessor (Surf-126) seems to be slightly superior to the general SURF system. However, its matching process was slow. Thus, it may be unsuitable for applications that require speed. Object recognition was performed under a similar set of standards and threshold element (Table 1). The moment was evaluated on a standard Unix computer (Pentium IV, 2.5GHZ). The objects are recognized because we experienced new functionalities on the practical application, aiming to identify the art object in the museum. The data consisted of 216 images of 22 objects. The test group images comprised 116 images.
Detecting | Threshold | Nb of indicators | Compu. time (ms) |
---|---|---|---|
Quick Hessian | 601 | 1417 | 119 |
Hessian-Laplace | 900 | 1980 | 651 |
Harris-Laplace | 2400 | 1665 | 1799 |
DoG | Default | 1521 | 401 |
Threshold element, numbers of points detected, and computational time (the first image of the graffiti sequence, 900 × 640).
U-SURF | SURF | SURF-126 | SIFT | |
---|---|---|---|---|
Time (ms) | 254 | 355 | 390 | 1035 |
Calculation time for common detectors—descriptive applications, testing on the first image of the Graffiti sequence.
The threshold element is adjusted to detect the same number of indicators of interest for all methods. The relatively shorter calculation time also represents the other image.
Under different conditions, including extreme illumination changes, object reflections in glass cabinetry, changes in perspective, magnification, and differences in camera quality, images are small (319 × 240) and difficult to recognize because they lose detail. To identify the objects in the database management, we proceed as follows: The images of the test group are compared with all the images of the reference group by associating their respective indicators of interest. The object represented on the reference image is selected with the greatest amount of correspondence with respect to the test image as a recognized object. Correspondence is performed as follows: A perspective of interest in the test image is compared with a perspective of interest in the referenced image by computing the value of Euclidean space between the vector and its descriptors. A corresponding pair is detected if the vision distance is closer by 0.6 times than that from the closest neighbor to the second. It is the closest strategy that corresponds to the ratio of the neighbors [2, 8, 27]. Extra engineering restrictions reduced the impact of false-positive matching, and this can be performed over any situation. For comparative reasoning, this does not make sense because it may be hiding the lack of the basic tables. On average, the rating reflection of the solutions of our performed appraisal is established. The leaders are SURF-126 with a recognizability rate of 85.7%, followed by U-SURF (84.8%), and SURF (83.7%). The other descriptors were 78.4% for GLOH, 78.2% for SIFT, and 72.3% for PCA-SIFT (Figures 10 and 11).
Example images of the reference group (left) and the test group (right). Note the difference in perspective and colors.
Left to right and from top to bottom: Frequency of Walls-Graffiti (perspective change), Leuven (illumination change), and Boats (magnification and rotation).
A brain-computer interface (BCI) is a direct interface between the human brain and an artificial system. Its purpose is to control the actuation of a device.
Many researchers have proposed modern algorithms to solve the problem of image authentication. This study explored and compared the application of different algorithms that detect common types of image forgery. The characteristics of the algorithms are shown in Table 2. The algorithms we examined in this study are undoubtedly important for the detection of image counterfeiting. Previous researchers have attempted to improve the reliability of image fraud detection algorithms. They have achieved this objective by (1) reducing algorithm complexity and computational time. This objective was achieved by using small vector dimensions, as shown in Refs. [18, 37, 38, 39, 40, 41] increasing the robustness of the algorithms. This aim was achieved by adopting a powerful feature that is consistent for a wide range of image processes, as shown in Refs. [42, 43, 44, 45, 46, 47, 48]. The algorithm based on fixed key indicators and fixed instances exhibits remarkable performance, as shown in Table 2. However, several barriers and challenges remain. We summarize the defects of available algorithms in Tables 1 and 2: (1) the algorithms cannot handle all possible types of image processing that can be applied to forge images; (2) some algorithms rely heavily on several threshold elements or initial value, and the identification of these threshold elements and values require experimentation and improvements; and (3) most current methods take time [49, 50]. The development of complex and reliable algorithms that quickly and rapidly detect image forgery has been proposed. However, future work must overcome the following challenges: (1) the lack of standardized datasets for false counterfeiting limits the comparability and reproduction of existing algorithms, as well the design of improved algorithms and (2) the lack of common quantitative methods for measuring and evaluating algorithm performance prevents the comparison of different algorithms under different conditions. We believe that this reason accounts for the absence of studies that compare the accuracy and performance of different algorithms. Given that detecting counterfeiting is still in its early stages, considerable work remains to be performed, and other ideas can be derived or borrowed from other fields, such as object recognition or image analysis (Figure 12).
Left to right and top to bottom: graphs of changes in 50 (Walls) grades, descale element 2 (Boats), image blur (Bikes and Trees), illumination level (Leuven), and JPEG compression (Ubc).
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In this chapter, I describe the similarities and differences between the naïve and primed pluripotent states as exemplified by mouse embryonic stem cells (mESCs), mouse epiblast stem cells (mEpiSCs), human embryonic stem cells (hESCs), and human induced pluripotent stem cells (hiPSCs). I also review the efforts for derivation of naïve human pluripotent stem cells by manipulating culture conditions during reprogramming of somatic cells and attempts to revert primed hESCs to the naïve state. Understanding the requirements for induction and maintenance of the naïve pluripotent state will facilitate studies on early human embryonic development and understanding the mechanisms involved in X inactivation in vitro. 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These cells remain in a quiescent state until they are activated by different factors, usually those generated by an alteration in the parenchymal tissue. These cells have characteristic membrane markers such as CD73, CD90, and CD105. Those are a receptor, which in response to their ligand induces strong changes in different metabolic pathways that lead to these cells, both to generate molecules with different activities and to leave their stationary phase to reproduce and even differentiate. 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It consists of a group of disorders showing a higher incidence and severity of infections, expression of immunological dysregulation such as inflammation and lymphoproliferation. The immunophenotyping and in vitro functional characterization of immunodeficient patients contribute, together with the clinical aspects, to define the underlying immune defect particularities. Flow cytometry applications in primary immunodeficiency assessment are multiple and include the study of a wide range of specific cell lymphocyte subpopulations. This chapter describes the main techniques used in the diagnosis of a wide variety of primary immunodeficiencies, in which intracellular proteins or activation markers involved in immunity are evaluated, as well as functional proliferation, cytokine production, phosphorylation of transcription factors, cytotoxic and degranulation capacity. 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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 gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. 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The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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