Quality and resolution of data features (aerial imagery) (Švajda et al., 2011).
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis 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.
\n\nWe 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.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\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:"4477",leadTitle:null,fullTitle:"Hypercholesterolemia",title:"Hypercholesterolemia",subtitle:null,reviewType:"peer-reviewed",abstract:"This book is aimed to accentuate the importance of hypercholesterolemia, since targeting and treating the hypercholesterolemia is increasingly well known as an essential strategy in the prevention of atherosclerosis-induced cardiovascular disease. It is important to look at hypercholesterolemia as it is proved to be crucial as well as the early stage of atherogenesis and can also be managed with appropriate treatment. This book describes the basics of hypercholesterolemia and its causes and various experimental animal models to understand and study the pathophysiology of hypercholesterolemia as well as to present practice-based clinical approaches to treat hypercholesterolemia. Further, the book describes various treatment strategies of hypercholesterolemia in detail, especially the appropriate use of statin. It is well known that the use of statin is an ideal as well as a potent therapy to lower cholesterol level and also has various beneficial pharmacological effects to prevent cardiovascular diseases. However, there exists less awareness about the use of statin. Hence, it is important to understand the appropriate use of statin in terms of doses for different stages of hypercholesterolemia, side effects, resistance of its use, and also interaction of statin with other drugs, which are well described in this book. 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Ashok Kumar is an Assistant Professor at Centre for Biotechnology, Anna University, Chennai, Tamil Nadu, India. He has done doctoral research on hypercholesterolemia and obtained Ph.D. from University of Madras. Later, he joined as a Post-doctoral fellow at CancerCare Manitoba, University of Manitoba. At present as an Assistant Professor at Anna University, he is actively involved in the research field of hypercholesterolemia and targeting signaling pathways in metabolic disorder. Dr. Kumar research work has been supported by grants from national funding agencies such as UGC and DST-SERB. His research findings were published in 9 international journals and a book chapter. 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\r\n\tLegume crops provide significant sources of plant-based proteins for humans. Grain legumes have outstanding nutritional and nutraceutical properties, while they are affordable food that contributes to achieving future food and feed security.
\r\n\r\n\tDue to an increasing world population, global food security requires a major re-focusing of plant sciences, crop improvement, and production agronomy towards grain legumes (pulse crops) over the coming decades, with intensive research and development to identify climate-resilient species and cultivars with improved grain characteristics. In this context, genetic developments have played a critical role to increase crop production, whose applications will undoubtedly contribute to sustainable agriculture and food security.
\r\n\r\n\tThis research topic aims to bring together a collection of outstanding studies for a better understanding of current improvements in agricultural and seed traits from both the biological (physiological and nutritional/nutraceutical) and genetic viewpoints. We welcome submissions of all types of articles falling under, but not limited to, the research topic highlighted in this book.
",isbn:"978-1-80356-915-4",printIsbn:"978-1-80356-914-7",pdfIsbn:"978-1-80356-916-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"f4fab812b83d12b4b9d72db2e0d92208",bookSignature:"Dr. Jose Carlos Jimenez-Lopez and Dr. Alfonso Clemente",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12236.jpg",keywords:"Orphan Crops, Sustainable Agriculture, SNPs, Legume Breeding, Genetic Diversity, Functional Foods, Seed Compounds, Food Security, Food Allergy, Abiotic & Biotic Stresses, Crop Resilience, Fungal Pathogens",numberOfDownloads:1271,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:1,numberOfTotalCitations:1,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 5th 2022",dateEndSecondStepPublish:"April 26th 2022",dateEndThirdStepPublish:"June 25th 2022",dateEndFourthStepPublish:"September 13th 2022",dateEndFifthStepPublish:"November 12th 2022",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"A pioneering researcher in legume seed proteins physiological and nutraceutical functions, appointed as Ramon y Cajal research fellow and tenured scientist at CSIC; AEL board member and holder of two registered patents.",coeditorOneBiosketch:"Scientist at the Spanish National Research Council, President of the Spanish Legume Association, and Author of more than 120 scientific manuscripts who has been working in legume seeds research for the last 20 years.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"33993",title:"Dr.",name:"Jose Carlos",middleName:null,surname:"Jimenez-Lopez",slug:"jose-carlos-jimenez-lopez",fullName:"Jose Carlos Jimenez-Lopez",profilePictureURL:"https://mts.intechopen.com/storage/users/33993/images/system/33993.jpg",biography:"Dr. Jose C. Jimenez-Lopez, BS. Biochemistry (1998), BS. Biological Sciences (2001), MS. Agricultural Sciences (2004), University of Granada, Spain; and Ph.D. Plant Cell Biology (2008) at CSIC. He was a Postdoctoral Research Associate at Purdue University, USA (2008-2011), and Marie Curie Research Fellow (EU - FP7) (2012-2015) at the University of Western Australia and CSIC. Currently, he is a Senior Research Fellow (Ramon y Cajal research program, 2016 - present), working in the functionality, health benefits, and molecular allergy aspects of seed proteins from crop species of agro-industrial interest (mainly legumes). He is the author of more than 65 journal articles, 29 book chapters, 2 patents, and more than 130 international congresses. He is an active member of different Scientific Societies and editor of multiple books.",institutionString:"Spanish National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"7",totalChapterViews:"0",totalEditedBooks:"7",institution:{name:"Spanish National Research Council",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"149660",title:"Dr.",name:"Alfonso",middleName:null,surname:"Clemente",slug:"alfonso-clemente",fullName:"Alfonso Clemente",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRDEMQA4/Profile_Picture_1615291343716",biography:"Dr. Alfonso Clemente is a staff scientist at the Spanish National Research Council, working at the Estación Experimental del Zaidín (Granada, Spain). He has been working in legume seeds for the last 20 years, being involved in several national and international related projects. Alfonso Clemente joined different labs (Institute of Food Research, 1999–2000; John Innes Centre, 2000–2002; Sainsbury Laboratory, 2003–2004) in the UK to broaden his laboratory skills and scientific knowledge. Currently, he is the President of the Spanish Legume Association (Asociación Española de Leguminosas, www.leguminosas.es) having strong interaction with a relevant network of scientists and agricultural associations and agri-food companies in the field. He is author of more than 120 scientific manuscripts and an editorial board member of the World Journal of Gastroenterology and Frontiers in Bioscience, among others.",institutionString:"Spanish National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:[{id:"81535",title:"Function of Urease in Plants with Reference to Legumes: A Review",slug:"function-of-urease-in-plants-with-reference-to-legumes-a-review",totalDownloads:19,totalCrossrefCites:0,authors:[null]},{id:"78515",title:"Bioactive Peptides from Legumes and Their Bioavailability",slug:"bioactive-peptides-from-legumes-and-their-bioavailability",totalDownloads:152,totalCrossrefCites:0,authors:[null]},{id:"76906",title:"Phenolic Compounds in Legumes: Composition, Processing and Gut Health",slug:"phenolic-compounds-in-legumes-composition-processing-and-gut-health",totalDownloads:108,totalCrossrefCites:0,authors:[null]},{id:"78185",title:"Health Risks Associated with the Consumption of Legumes Contaminated with Pesticides and Heavy Metals",slug:"health-risks-associated-with-the-consumption-of-legumes-contaminated-with-pesticides-and-heavy-metal",totalDownloads:101,totalCrossrefCites:0,authors:[null]},{id:"79589",title:"Nutraceutical Properties of Legume Seeds: Phytochemical Compounds",slug:"nutraceutical-properties-of-legume-seeds-phytochemical-compounds",totalDownloads:93,totalCrossrefCites:0,authors:[null]},{id:"80561",title:"Fermentation as Strategy for Improving Nutritional, Functional, Technological, and Sensory Properties of Legumes",slug:"fermentation-as-strategy-for-improving-nutritional-functional-technological-and-sensory-properties-o",totalDownloads:86,totalCrossrefCites:0,authors:[null]},{id:"79107",title:"Legumes, Sustainable Alternative Protein Sources for Aquafeeds",slug:"legumes-sustainable-alternative-protein-sources-for-aquafeeds",totalDownloads:142,totalCrossrefCites:0,authors:[null]},{id:"77832",title:"Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (Lablab purpureus L.): Role of OMICS Based Biology, Biotic and Abiotic Elicitors",slug:"unlocking-pharmacological-and-therapeutic-potential-of-hyacinth-bean-lablab-purpureus-l-role-of-omic",totalDownloads:158,totalCrossrefCites:0,authors:[null]},{id:"78557",title:"Soybean and Other Legume Proteins Exhibit Beneficial Physiological Effects on Metabolic Syndrome and Inflammatory-Related Disorders",slug:"soybean-and-other-legume-proteins-exhibit-beneficial-physiological-effects-on-metabolic-syndrome-and",totalDownloads:72,totalCrossrefCites:0,authors:[null]},{id:"79467",title:"A Review on the Cooking Attributes of African Yam Bean (Sphenostylis stenocarpa)",slug:"a-review-on-the-cooking-attributes-of-african-yam-bean-sphenostylis-stenocarpa",totalDownloads:98,totalCrossrefCites:0,authors:[null]},{id:"80999",title:"Vigna unguiculata (L.) 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Jimenez-Lopez",coverURL:"https://cdn.intechopen.com/books/images_new/5382.jpg",editedByType:"Edited by",editors:[{id:"33993",title:"Dr.",name:"Jose Carlos",surname:"Jimenez-Lopez",slug:"jose-carlos-jimenez-lopez",fullName:"Jose Carlos Jimenez-Lopez"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1819",title:"Biochemical Testing",subtitle:null,isOpenForSubmission:!1,hash:"bab205c706b0f34b0dfcfa1196437fcf",slug:"biochemical-testing",bookSignature:"Jose C. 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The growth in the global mean surface temperature by 0,74 °C ± 0,18 °C, over the last 100 years (1906-2005) is probably related to greenhouse gas emissions and further warming will cause many changes in the global climate system during the 21st century(IPCC, 2007). These changes will affect both the abiotic and biotic conditions of the environment.
High mountains ecosystems represent unique areas for the detection of climate change and the assessment of climate-related impacts (Beniston, 2003). Climate change associated with global warming at higher elevations is more pronounced than at low elevations (Beniston&Rebetez, 1996; Giorgi et al., 1997; Diaz & Bradley, 1997). The effect of elevation on surface warming is especially marked in the winter and spring seasons, since it is mostly associated with a decrease in snowpack and is thus enhanced by the snow–albedo feedback (Giorgi et al., 1997). The main ecological driving force is climate, with temperature and the duration of the snow cover as key factors (Gottfried et al., 1999). Changes in air temperature can extend the length of the average annual growing season (Menzel&Fabian, 1999) and can also cause a shift in phenology (Parmesan &Yohe, 2003; Visser& Both, 2005).
Climate change is an important driving force on natural systems (Parmesan &Yohe, 2003). Many studies show that high mountain ecosystems are vulnerable to climate change (e.g. Theurillat&Guisan, 2001; Dullinger et al., 2003a, 2003b; Dirnböck et al., 2011). Global climate change resulting in warmer climate may cause a variety of risks to mountain habitats (Beniston, 2003). Climate change mainly affects the distribution of plant and animal communities (Beckage et al., 2008) and under the expected climate scenarios in the final perspective results in the loss of rare species of alpine habitats (Dirnböck et al., 2011). In the global meta-analyses from Parmesan &Yohe (2003) and Root et al. (2003) significant range shifts toward the poles or toward higher altitudes for many organisms were documented. Large part of these changes may be attributed to increased global temperatures.
In general terms, we expected that climate related changes in mountain ecosystems will be most pronounced in the "ecoclines" (boundary ecosystem), or Ecotones (Theurillat&Guisan, 2001). Distribution of endemic mountain species is typically severely restricted as a spatial response in mountain areas, however, because of mountain topography (Huntley & Baxter, 2002) and, often, the availability of suitable soils (Theurillat et al., 1998). The upper forest limit is commonly referred to as tree line. Timberline or forest line represents one of the most obvious vegetation boundaries (ecoclines). In reality the transition from the uppermost closed montane forests to the treeless alpine vegetation is commonly not a line, but a steep gradient of increasing stand fragmentation and stuntedness, often called the tree line ecotone or the tree line park land (Körner& Paulsen, 2004).
Scenarios of upward plant species and vegetation shifts are widely discussed in many current research articles. Theurillat&Guisan (2001) released a review discussing this matter concluding that although the alpine vegetation can tolerate an increase of 1-2 °C of average air temperature, in the case of a sharper increase we can expect major changes. Loss of diversity of the alpine communities and fragmentation of plant populations caused by climate warming is expected for comparable high mountains around the world (Grabherr et al, 1995; Sætersdal et al., 1998).
Results from Dirnböck et al. (2003) support the hypothesis, that alpine plant species above the forest line will be affected by heavy fragmentation and habitat loss, but only if the average annual temperature increases by 2 °C or more. Most of these lost alpine plant species habitats are expected to be caused by the expansion of
Lapin et al. (2005) detected climate changes in the Slovak mountains. The results showed a significant increase in temperature and a decrease in relative humidity in the April to August season after 1990. From 1901–2005, air temperature increased (annual mean) moderately and precipitation decreased (Melo, 2007). This trend of warming is expected to continue in the Slovak mountains. By 2075 the annual average air temperature in Slovakia is expected to increase by 2-4 ° C (with greater warming expected in the winter) and more significant effects of increasing temperatures is expected at the higher altitudes (Mindáš&Škvarenina, 2003). Generally, climate conditions and land use in high mountain areas have been shown to influence the distribution of mountain pine. Since 1965 the ban on grazing in the High Tatras has not yet been raised. This study assesses a potential scenario after the grazing in Tatra Mountains will have been resumed. The potential model of timberline is based on the assumption that climate change as a factor in forest regeneration is primarily responsible for moving the upper limit of the natural forest above the original climatically determined timberline, while the abandonment of farming in the country should be the dominant factor determining forest regeneration below this line.
The Tatra Mountains are situated at the Slovak–Polish border (20°10′E, 49°10′N) and constitute the highest mountain massif within the Carpathian Range of Central Europe. The highest summit reaches 2656 m; the massif is classified as a high-mountain landscape covered by subalpine and alpine zones.
The study area (Figure 1) is situated in the western Tatra Mountains. The geology of the investigated area is based on crystalline bedrock. The western Tatra Mountains contain a significant amount of metamorphics (gneiss and mica schist), in addition to granodiorite(Nemčok et al., 1993). The vegetation of the alpine zone is dominated by alpine meadows (dry tundra with mostly
The average annual air temperature decreases with elevation by 0.6°C per 100 m, being 1.6 and 23.8°C at elevations of 1778 and 2635 m, respectively (Konček&Orlicz, 1974). The amount of precipitation increases with elevation, varying from ~1.0 to ~1.6 m yr-1 between 1330 and 2635 masl but reaching >2.00 m yr-1 21 in some valleys (Chomitz&Šamaj, 1974). Precipitation is generally higher in the northern part than in the southern part of the mountains, as is runoff, which averages 1.42 and 1.57 m yr-1 for the south and north, respectively (Lajczak, 1996). Snow cover usually lasts from October to June at elevations > 2000 masl.
Western Tatra Mountains in Slovakia and detailed view of the 25 sites in the study area (from west to east: Roháče, Baníkov, Baranec, Bystrá, Jamnická, Račkova, Kamenistá, Tichá, Kôprová, and Špania valleys). (Map by JaroslavSolár)
The relationship between climate conditions of the environment (microclimate and vertical climate) and phytocenoses is expressed at different altitudinal zones in the forest. Constant climate conditions definitively influenced the natural distribution of forest species from the sub-Atlantic period (around 2000 years ago), when the current altitudinal zones were formed. Significant changes of forest stratification were caused by the intense human activity since the 13th century. Ecologically, forest altitudinal zones represent vertical classification of vegetation. Horizontal classification is determined by growth condition of forest societies, differentiated especially according to soil conditions, ecological rows, interrows, and hydric files of forest type groups. The climate-driven tree line in the Tatra Mountains is located around 1550 masl and partly includes natural ecotones with individual conifers reaching ages of 350–450 years (Büntgen et al., 2007).
On slovakia in the period 1881-2007 was increase of annual temperature in 1,6°C and annual precipitation decrease in 24 mm (Lapin et al., 2009). The temperature series show an upward trend in all seasons, especially in the spring (Melo et al., 2009). Over the past 20 years, it seems much warmer and especially in the months of January to August (Faško et al., 2008). Warming scenarios based on applied GCM (General Circulation Model) for Slovakia represent the increase in average annual temperature of 2-4 °C until the end of the 21st century (Melo et al., 2009).The climate in the Slovak mountain region is thus becoming warmer. Figure 2 shows a general trend that could partially explain the dynamics of the vegetation zones.
Winter precipitation in the high-mountain positions of Slovakia is abundant and increases with altitude. (Ostrožlík, 2008, 2010). Sensitivity of snow cover will vary depending on the climate and altitude. Also sensitivity causes maritime climates and less continental climate of cold and dry winters, where precipitations play an important role in the variability of snow cover duration. (Brown & Mote, 2009). The Tatra Mountain have significanly more snow cover days on the northern slopes. More over the less windy and forested areas have higher and longer snow cover as well. (Lapin et al., 2007). Snow cover duration on the northern slopes is critical in altitude of about 1800 m and on the southern slopes of about 2300 m. In this altitudinal level in Tatra mountain should be zone, above which would not even occur to the loss of snow cover duration. (Vojtek et al., 2003). It seems that variability and trends in snow cover characteristics are influenced both by air temperature and precipitation variability. This influence depends significantly on the altitude and local topography conditions. Increase of air temperature by about 1,2 °C and change of precipitation totals from -10% to -20% in the November-April season are the main reasons of obtained trends (Lapin et al., 2007).
Trends in annual average temperature (in degrees Celsius) and annual total precipitation (in millimeters) from 1965 to 2002 at the meteorological station of Skalnate´ pleso (1751 masl), Slovak Institute of Hydrometeorology (
Changes in landscape can be well observed through remote sensing (RS). RS data (images, aerial photographs, etc.) are further processed and analyzed using Geographic Information Systems (GIS). Progressive development of geo-information technologies offer new approaches to the use of remote sensing in GIS. GIS is very useful for its ability to incorporate the complexity of spatial data in to the various models. Remote-sensing based analysis is particularly useful in mountainous areas where the topography is complex and different environmental gradients require special attention to the spatial patterns (Heywood et al., 1994). Although high mountain environments show a high degree of heterogeneity, we can obtain satisfactory results using the appropriate approaches and high-quality materials. Changes in the natural spatial (morphological, bioenergetical) features can be identified in remote sensing images (Feranec et al., 1997). Using GIS applied approach let us identify the most significant variables and phenomena which affect the natural environment components.
Approach of remote sensing and suitability of this method in detecting of lanscape changes in mountain regions of Slovakia was confirmed in the works: Boltižiar, (2001, 2002, 2003, 2004, 2006, 2007); Čerňanský&Kožuch, (2001); Hreško&Boltižiar, (2001); Kohút, (2006); Olah et al., (2006); Falťan&Saksa, (2007); Olah&Boltižiar, (2009). However, this approach can be difficult in countries with politically sensitive situation, where the products of remote sensing are subject to various degrees of secrecy (Heywood et al., 1994). We have come across some other problems arise in relation to data quality and its precision of position placement, which takes into account the high diversity of the relief. Advantage of access interpolation of aerial imagery lies in the fact, that we can carry out their research in a relatively short time. Especially aerial photographs provides a large amount of quantitative and qualitative information about the landscape structure and are particularly important in high mountain areas, where field research is difficult (Boltižiar, 2009).
This study is based on results published in the original study by Švajda, Solár, Janiga& Buliak „Dwarf Pine (
Aerial images from 2002 were acquired and georeferenced by Eurosense Ltd. and Geodis Slovakia on the basis of contour lines at a scale 1:10,000 (digital elevation model); we georeferenced aerial photographs from 1986 and 1965 on the basis of orthophotos (Table 1).
Year | Source | Type | Resolution | Width | Height | Format |
2002 | Eurosense Slovakia | Orthophoto | RBG 72 DPI | 2500 | 2000 | .jpg |
1986 | Topographical Institute | Aerial photo | Gray 2400 DPI | 21.829 | 21.924 | .tiff |
1965 | Knazovicky | Aerial photo | Gray 300 DPI | 2164 | 2175 | .jpg |
DPI. dots per inch; RBG. red green blue. |
Quality and resolution of data features (aerial imagery) (Švajda et al., 2011).
Mountain pine fields were extracted from the aerial photos in gray scale and than reclassified into gray scale range representing mountain pine occurrence in the study area. Each photo was examined individually. If mountain pine on the slide was gray, with a value from 75 to 110, all such values in the range were reclassified as 1. The remaining values from 0 to 75 and 110 to 256 were reclassified as 0. We created a grid where each pixel contained either the value 1 or the value 0. Then the grid was automatically vectorized on the basis of the 2 values.
Habitat conditions were spatially simulated using GIS, digital terrain model, meteorological data and existing maps. In addition we analyzed historical records in order to derive information about past land-use changes. The most significant factors explaining the presence of
To test this hypothesis it was necessary to create an explicit temporal and spatial explicit model of the spread of mountain pine and analyze their sensitivity to predicted climate change trends. Histogram transformation was not carried out due to the misrepresentation of values. The size of the pixels’ (cell) grid was equivalentin all RS images, because each image was adjusted to the same size cell size through the transformation of the grid, as well as during georeferencing. Thus all images and the grids had the same pixels (Figure 3).
Selection of the appropriate areas, which represented 25 localities from the study area, and analysis of imagery were realized in ArcGIS 9.2. Differences in the
The increments in dwarf pine were reported as means and standard deviations for potential comparison with other studies, but the values showed a highly skewed distribution in most sample groups. Therefore, a nonparametric approach to the analysis of the data was necessary. The significance of difference between groups was tested using the Kruskal–Wallis nonparametric test. When P, 0.05, the data were considered as significantly different. A digital elevation model of the study area was used for the representation of a selected abiotic habitat conditions. A single matrix was analyzed. GIS intersection of study sites with 3 parameters (slope, aspect, and height masl) has divided the studied sites into 325 smaller areas with unique characteristics related to pine increase. Two sites (nos. 5 and 9; Table 2) were excluded due to lack of data from 1986.
The principal component analysis (PCA)–correlation matrix, a multivariate technique was used to extract the potential relationships between the studied variables. Principal components are linear combinations of original variables (slope, height masl, and relative increase of pine during observed periods), each axis being statistically orthogonal to the others. Integration of the variables slope and elevation m asl in different periods enabled us to follow different processes of mountain colonization by mountain pine during the respective periods. Since this statisticale technique produces statistically orthogonal axes, we were able to examine potentially independent biological phenomena. We used 4 variables; consequently, we evaluated 4 principal components. The proportions of the total variance accounted for by each component are shown in Table 3 (see results).
Example of the comparison between aerial photographs: changes between 1965, 1986, and 2002 in 1 analyzed valley (Site 17, Račková valley;
Site number | Average altitude (masl) | Average slope (%) | Covered with | Difference (+/-) |
1 | 1674 | 34 | 27 | +1/+20 |
2 | 1670 | 46 | 7 | +9/+11 |
3 | 1614 | 44 | 29 | +9/+23 |
4 | 1675 | 36 | 24 | +31/+41 |
5 (excluded) | 1686 | 42 | — | — |
6 | 1614 | 33 | 35 | +8/+-10 |
7 | 1733 | 28 | 28 | +4/+9 |
8 | 1604 | 40 | 53 | +11/+13 |
9 (excluded) | 1807 | 34 | — | — |
10 | 1825 | 22 | 16 | +6/+16 |
11 | 1511 | 47 | 61 | +14/+14 |
12 | 1533 | 43 | 68 | +12A16 |
13 | 1493 | 33 | 47 | +8/+10 |
14 | 1684 | 40 | 19 | +4/+12 |
15 | 1617 | 46 | 21 | +11/+13 |
16 | 1561 | 43 | 62 | +3/+10 |
17 | 1640 | 45 | 48 | +18/+28 |
18 | 1777 | 35 | 34 | +6/+11 |
19 | 1595 | 55 | 45 | +10/+12 |
20 | 1627 | 30 | 42 | +6/+23 |
21 | 1449 | 33 | 49 | +21/+20 |
22 | 1632 | 45 | 42 | +11/+31 |
23 | 1599 | 36 | 68 | +11/+20 |
24 | 1428 | 31 | 59 | +20/+16 |
25 | 1533 | 38 | 65 | -2/+2 |
Overview of evaluated sites with different
Mountain pine cover in the western Tatra Mountains in the period 1965–2002 permanently increased at all observed sites. The total surface area covered by mountain pine increased from 8,173,812 m2 in 1965 to 10,141,505 m2 in 1986 and 11,394,461 m2 in 2002. The percentage of total surface area covered thus increased from 41.8% in 1965 to 51.8% in 1986 and 58.2% in 2002. Only in one case (No. 25) surface area covered by dwarf pine decreased. In two cases (No 21 and 24) the area decreased in the first, and increased in the second period (Table 2, Figure 4). This was probably due to the influence by human activities or avalanches.
The results also indicate that the mean increase of mountain pine surface cover was in all periods about 0.4 percent per year (0.42% first period, 0.40% second period) from the total surface area but results in relation to selected abiotic conditions still showed some differences.
Comparison of area covered with
From 1965 to 1986, mountain pine showed a rapid expansion in surface cover at the lower elevations (Table 3 – PC1, Figure 5A). This could be observed as thickening of mountain pine cover at lower elevations, indicating that the mountain pine is able to recolonize sites of previous occurrence.
0.51 | -0.64 | -0.50 | 0.26 | |
-0.71 | 0.11 | -0.63 | -0.27 | |
0.73 | 0.14 | -0.10 | -0.64 | |
-0.38 | -0.78 | 0.30 | -0.37 | |
36.5 | 26.8 | 18.9 | 17.8 | |
Component vectors (loadings) and percent variance associated with the components indicating the pattern of natural reforestation with dwarf pine in the Tatra Mountains (n 5 325; snaps from aerial photographs) (Švajda et al., 2011).
In the period from 1986 to 2002, pine grew rapidly on steeper slopes (Table 3 – PC2), mainly at elevations from 1500 to 1700 masl (Figure 5B). During the first analyzed period, mountain pine was able to concentrate to such a level at elevations between 1300–1400 m a.s.l. that in the following period it completely covered this zone and further increments were minimal.
The third factor (Table 3 - PC3) is less important for the explanation of the historical pine increments: it shows a positive relation between slope and elevation. PC4 (Table 3) is a unipolar vector indicating that mountain pine grew more rapidly in the earlier period (1965–1986) than later (1986–2002).
In the earlier period mountain pine grew intensely at all locations (Figure 5C) whereas in the period 1986–2002 it mainly preferred northwest and northeast aspects on steeper slopes, probably the most suitable locations for plant development in the Tatra Mountains. These sites might have more favorable conditions for the growth of the mountain pine due to changes in climate in terms of higher surface temperature of environment.
Increments in dwarf pine cover in the western Tatra Mountains in the periods 1965–1986 and 1986–2002, according to (A and B) elevation and (C and D) aspect. In the earlier period, the groups did not differ according to aspect. (C) Kruskal–Wallis nonparametric test at p = 0.05. (D) In the period 1986–2002, the following aspects differed significantly: N:NE, N:SW, NE:W, NE:S, NE:SE, SE:SW, S:SW, and SW:W (
In both periods, the increments in the areas covered by mountain pine were very low at the elevation of 1900 m, reflecting its natural upper line of occurrence. In the earlier monitored period the increments at 1900 m were 0%, whereas between 1986 and 2002 they were approximately 3% (Figures 5A, 4B). The trend is probably associated with climate warming in the region. Changed enviromental conditions caused by climate change promote the expansion of mountain pine and favour it in competition against alpine meadow communities.
The interaction of individual components of the environment is an ongoing process. The result of this interaction as seen in our results, show an apparent shift of mountain pine to higher altitudes. The expansion of mountain pine was confirmed by remote sensing. The precision of our results was limited by the fact that we performed a very fast automatic extraction of mountain pine fields. However, this analysis was repeated several times in order to avoid any errors during the extraction of mountain pine fields. We also recorded other factors which might affect the results of the distribution and growth of dwarf pine. This mainly relates to landslides, avlanches, snow cover and shadows from clouds or hills on the air photographs. Generally, a place where we have identified these problems, we excluded from the assessment in all of times periods. Due to aim of this study was not to highlight the processes that operate in the opposite direction to the expansion of mountain pine, so we did not dealt with this problem more. We had some problems in the lower parts of fields where the scrub of mountain pine interleaved with spruce forest. Analysis of this border and its response to climate change would be also interesting. We can assume, that spruce forest has pushed the lower limit of mountain pine to the higher altitudes (Mihai et al., 2007). But this process is slower than the expansion of mountain pine due to problems with the successful survival of spruce seedlings and seed production (Dullinger et al., 2005). Evidence of spruce forest move to higher altitudes was shown by Mihai et al. (2007) in the Southern Carpathians of Romania. In comparison to spruce stands the mountain pine cover in our study represented comparatively homogeneous areas easy to extract in our aerial images.
Similarly to other high mountains also the Carpathians show a trend of climate change and possibly the shift of vegetation types with altitude. Expected changes in tree line boundaries are evident in Carpathians but also other mountain ranges around the world, which could present a threat to the habitats of many rare species in the future. Over the last 50 years, summer temperatures in the Tatra Mountains summer temperatures have increased by 0.7 ° C at higher elevations, and 1.4 ° C at lower elevations. Winter temperatures have increased by 1.4 ° C at higher elevations, and 1.9 ° C at lower elevations (Melo, 2005). The temperature limit of the mountain pine zone is determined by the bio-temperature threshold in the range of 3.0 to 2.0 ° C (°C Max - Min ° C) (Miňdáš&Škvarenina, 2003). Considering the rate of current temperature changes (2-3 °C for 100 years) we can expect more turmoil changes to the growth within a single generation of woody plants. According to Miňdáš et al. (1996) a model scenario expects a complete extinction of conditions for alpine communities and their replacement by bioclimatic conditions for sub-alpine forest. The occurrence of mountain pine is subject to extreme habitat conditions, including soil. Mountain pine is a strongly heliophilic shrub. The most important factor of habitat which has a decisive role in the expansion of mountain pine is the light intensity. The spreading of mountain pine is conditioned mainly by altitude, slope, moisture conditions, but also the horizontal and vertical slope curvature. This can be seen in the fact that the mountain pine is spreading up along the ridges.
Minďáš et al. (2004) predict the following changes in an area of mountain pine zone timberline: (1) an increase in the abundance of tree species, (2) dominant representation of spruce, (3) a decrease of dwarf pine, and (4) an increase of general production and biomass of about 200–300%. These changes could also be contributed by the changes in phenological phases, which reflect the changing climate condtions. The onset of individual phenological stages and their proceeding is mainly influenced by air temperature, as well as temperature and humidity of soil and other meteorological variables (Škvareninová, 2009). Development of climate can to some extent affect phenological trends (Bauer 2006; Škvareninová, 2008) and identyfying these relationships can help us use trees as bio-climatic indicators of climate change (Škvareninová, 2009).At high altitudes the vegetation is under constant environmental stress and thus abiotic conditions become more important for the community development than biotic relationships (Pauli et al., 1996).
The main results of our case study confirm the results of previous research on mountain vegetation zones in the Slovak Tatras. Boltižiar (2007) analyzed spatiotemporal landscape structure change in the alpine environment of the Tatra Mountains. The landscape structure in 1949 in the study area was dominated by grassland, which resulted mainly from human activity. Statistical analysis of thematic maps from 2003 suggests extension of mountain pine cover, advance of forest, and reduction of grassland areas. Martazinova et al. (2009) conducted research on grasslands above the upper forest limit in the Ukrainian Carpathians. Grass cover significantly decreased in the sites whit conifer presence. Spruce stands mainly on the northern slopes moved to higher altitudes, while the beech stands in the same area on the southern slopes did not show any significant movement. Apparently the greatest changes were recorded at those sites where upper forest limit was marked at higher elevations. In their study of alpine, subalpine, and forest landscapes in the Iezer Mountains (southern Carpathians), Mihai et al. (2007) described how mountain pine–subalpine associations developed and gradually covered subalpine meadows and barren land (between 1986 and 2002, colonization averaged 0.14 km2/y). This might be important in the context of the surface of the subalpine and alpine zones in the mountains. However, mountain pine area has lost some lower stands because of spruce forests, which increased in elevation. This is largely a feature of southern aspect slopes (sunny), where the natural timberline is under some local conditions higher. It is also related to shorter duration of snow cover on the southern slopes (Lapin et al., 2007). Peneuelas et al., (2007) also observed a shift and change in the distribution of species on the tree line in the Montseny Mountains (Span). As observed from historic photographs for the last 60 years, beech stands significantly increased in abundance, which is reflected in the shift of this species to higher altitudes by about 30-50 meters.
However, there are interesting comparisons with studies from other European mountains. The results of the study conducted by Dirnböck et al. (2003) support earlier hypotheses that alpine plant species on mountain ranges with restricted habitat availability above the tree line will experience severe fragmentation and habitat loss, but only if the mean annual temperature increases by 2 °C or more. Even in temperate alpine regions, it is important to consider precipitation, in addition to temperature, when climate impacts are to be assessed. Another example from the Alps in Austria (Dullinger et al., 2004), after running a model for 1000 years, predicted that the area covered by pines will increase from 10% to between 24% and 59% of the studied landscape. The shape of the dispersal curve and spatial patterns of competitively controlled recruitment suppression affect range size dynamics at least as severely as does variation in assumed future mean annual temperature (between 0 and 2°C above the current mean). Moreover, invasibility and shape of the dispersal curve interacted with each other due to the spatial patterns of vegetation cover in the region. Dullinger et al. (2003a) indicated that a shift of tree and shrub species caused by landuse and expected climate change can be expected in the European Alps. Abandonment of pasture will allow invasive expansion of
In addition to climate change, human land use may drive changes in tree line. Land use in subalpine and alpine areas (grazing and extraction) affects the distribution of flora just as much as climate. Since the 13th–14th century, anthropogenic land cover change has involved clearing mountain-pine thickets to obtain new pastures for sheep and cattle grazing, for extensive charcoal and oil production, and for copper and iron-ore mining, sometimes leading to degradation. Jodłowski (2007) described how establishing national parks in the Tatras—Babia Góra and Giant Mountains enabled secondary succession, which has led to colonization of previously abandoned habitats. However, these processes have been hampered by harsh edaphic and climatic conditions as well as by avalanches and debris flows. Extensive planting of mountain pine in former Czechoslovakia significantly facilitated the regeneration of mountain pine thickets. After the absolute restriction of grazing in some national parks, we observed progressive long-term trends in secondary succession and patterns of plant establishment driven by climate.
Closed mountain pine thickets stretch up to 300 m above timberline, reaching approximately 1600–1750 masl in the Tatras and encompassing the upper part of the forest-alpine tundra ecotone. Habitats in the peripheral or isolated mountain belts at or above the tree line are generally rich in diversity of endemic species. In these habitats, tree line expansion disproportionally reduces habitats of high-altitude species. Such legacies of climate history, which may aggravate extinction risks under future climate change, have to be expected for many temperate mountain ranges (Dirnböck et al., 2011). Minimizing greenhouse gas emissions effectively in order to reduce climate warming, and thus the expansion of tree line species to higher altitudes.Furthermore, slowing down forest expansion by land use. The maintenance of large summer farms may contribute to preventing the expected loss of nonforest habitats for alpine plant species and might provide additional refuges for those endemic species which can survive in managed habitats (Dirnböck et al., 2003).
Our study shows an apparent shift and densification of
More research on vegetation dynamics in Slovakia’s mountain areas is needed in light of the significance of vegetation in the context of global change. The results of our study can be used not only as a baseline for future research to test possible climate change influences (resulting upward shifts compared to a potential surface size and trends in approach of dwarf pine extension) but also to compare trends in other mountainous areas. Further understanding of dispersal, persistence, and survival strategies of mountain pine in the western Carpathians is also required. We will continue to monitor dispersal of
This research was partly supported by the European Economic Area and Norwegian financial mechanism grant SK-00061. We thank Dr L. Kňazovický for aerial photos from 1965. Let us also thank to authors Dr. J. Švajda, Prof. M. Janiga and M. Buliak who created the previous study entitled "Dwarf Pine (Pinusmugo) and Selected Habitat Abiotic Conditions in the Western Tatra Mountains" published in journal Mountain Research and Development 31/3 in the year 2011.
Hand Gestures play very significant roles in our day-to-day communication, and often they convey more than words. As technology and information are growing rapidly in every sector of our life, interaction with machines has become an unavoidable part of life. Thus, a deep urge for natural interaction with machines is growing all around [1, 2]. One of the biggest accomplishments in the domain of Hand Gesture Recognition (HGR) is Sign language recognition (SLR) where machines interpret the static hand posture of a human standing in front of a camera [3]. Recently, implementation of HGR-based automotive interface in BMW cars is very much appreciated. Here, five gestures are used for contactless control of music volume and incoming calls while driving [4]. Project Soli is the ongoing project of Google’s Advanced Technology; in this project a miniature radar is developed that understands the real-time motion of the human hand at various scales [5].
Hand gestures are very versatile as they comprise static as well as dynamic characteristics, physical as well as behavioral characteristics, for example, movement in any direction, fingers can bend to many angles. Hand skeleton has a complex structure with a very high freedom factor, and thus its two-dimensional RGB data sequence has unpredictable variations. Visual recognition of dynamic hand gestures is complex because the complete process requires the determination of hand posture along with a cognitive estimation of the trajectory of motion of that posture [3, 6, 7, 8, 9]. Due to these intricacies to date, vision-based HGR applications mainly dominate with static hand gesture recognition.
In context with computer vision and pattern recognition, a human hand is described as a biological target with a complex structure. Uneven surface, broken contours, and erratic pattern of movement are some of the natural characteristics that complicate DHGR [10]. Thus, in comparison to the other commonly tracked moving object, a hand is a non-rigid subtle object and covers a very small area in the image frame. The scientific challenges accompanied in the online tracking of the hand region in an unconstrained environment in RGB images captured using a simple camera are categorized as follows: [3, 4, 6, 7, 8, 9, 10, 11].
Intrinsic Challenges: Intrinsic challenges are related to a target that is “Hand” physical and behavioral nature. The features such as
Hand Appearance: The number of joints in the hand skeleton, the appearance of the same hand posture has a large variation, known as shape deformation. Different postures have a wide difference in occupancy area in an image frame, and some postures only cover 10% of the image frame, which is a very small target size in computer vision. In a real-time unconstrained environment, the two-dimensional (2-D) posture shows large variation during movement.
Manner of Movement: There is a large diversity among human beings in performing the gesture of the same meaning, in terms of speed and path of movement. The moving pattern of the hand is erratic, irregular, and produces blur in the image sequence. Furthermore, the two-dimensional data sequence of a moving hand is greatly affected by background conditions, thus tracking and interpretation of dynamic hand gestures are a challenging task in the HGR domain. The unpredictable variation in target trajectory makes the detection and classification process complex in pattern recognition.
Extrinsic Challenges: These challenges mainly arise due to the environment in which the hand movement is captured. Some of the major factors that deeply impact the real-time visual tracking of the dynamic hand gestures are as follows:
Background: In the real-time HGR applications, backgrounds are unconstrained, we cannot use fixed background models to differentiate between the foreground and the background. Thus, the core challenge in the design of a real-time hand tracking system is the estimation of discriminative features between background and target hand posture.
Illumination: Illumination conditions in real-time applications are uneven and also unstable. Thus, 2-D (two-dimensional) projection of the 3-D (three-dimensional) hand movement produces loss of information in RGB images. This loss is the major reason for errors in the visual tracking of hand movement.
Presence of other skin color objects in the surroundings: The presence of objects with similar RGB values such as the face, neck, arm, etc., is the serious cause for track loss in the RGB-based visual tracking techniques.
There are four main components in cognitive recognition of dynamic hand gestures [3, 10, 11, 12].
Data Acquisition.
Interest Region Detection.
Tracking of Interest Region.
Classification of Trajectory.
In Dynamic Hand Gesture Recognition (DHGR), acquisition of signals plays a very important role in deciding the technique to recognize and deduce the hand pattern into meaningful information. Contact-based sensors and contactless sensors are two main types of sensors to acquire hand movement signals. Contact-based sensors are those sensors that are attached to the body parts of a user example. Data gloves are hand gloves, accelerometers are attached the arm region, and egocentric sensors are put on the head to record hand movement. Wearable sensor devices are equipped with inertial, magnetic sensors, mechanical, ultrasonic, or barometric [7]. Andrea Bandini et al. [13], in their survey, presented many advantages of egocentric vision-based techniques as they can acquire hand signals very closely. Although the contact-based techniques require fewer computations, but wearing these devices gives uneasiness to the subject. Due to the electrical and magnetic emission of signals, it is likely to produce hazardous effects on the human body.
Contactless sensors or vision-based sensor technology is becoming encouraging technology to develop natural human-machine interfaces [1, 2, 3, 4, 14]. These devices consist of visual sensors, with a single or a group of cameras situated at a distance from the user to record the hand movement. In vision-based methods, the acquired data is image type, a user does not have to wear any devices, and he can move his hand naturally in an unconstrained pattern. The important assets of vision-based techniques are large flexibility for users, low hardware requirements, and no health issues. These methods have the potential to develop any natural interface for remote human-machine interaction, this can ease the living of physically challenged or elderly people with impaired mobility [2, 9, 15].
In vision-based methods, the information is two-dimensional, three-dimensional, or multiview images. Two-dimensional images are RGB images with only intensity information about the object, captured using simple cameras and. Three-dimensional images are captured from advanced sensor cameras such as Kinect, Leap Motion, Time of flight, etc.; these cameras collect RGB along with depth information of the object in the scene. The third and the most popular choice in HGR is multiview images; here two or more cameras are placed at different angles to capture the hand movement from many views [3, 6, 8].
Wang J. et al. [16] used two calibrated cameras to record hand gestures under stable lighting conditions. They initially segmented the hand region using YCbCr color space and then applied SIFT algorithm for feature extraction. After then, they tracked using Kalman Filter. But due to similarity with other objects, the author imposes position constraints to avoid track loss.
Poon G. et al. [17] also supported multiple camera setups that can observe the hand region from diversified angles to minimize the errors due to self-occlusion. They proposed three camera setups to recognize bimanual gestures in HGR. Similarly, Bautista A.G. et al. [18] used three cameras in their system to avoid complex background and illumination. Marin G. et al. [19] suggested combining Kinect data with Leap motion camera data to exploit the complementary characteristics of both the cameras. Kainz O. et al. [20] combined leap motion sensor signals and surface electromyography signals to propose a hand tracking scheme.
Andreas Aristidou discussed that high complexity in hand structure and movement make the animation of a hand model a challenge. They preferred a marker-based optical motion capture system to acquire the orientation of the hand [21]. With the same opinion, Lizy Abraham et al. [22] placed infrared LEDs on the hand to improve the consistency of accuracy in tracking. According to the study conducted by Mais Yasen et al. [9], surface electromyography (sEMG) as wearable sensors and Artificial Neural Network (ANN) as classifiers are the most preferable choices in hand gesture recognition.
The important factor in HGR is that information obtained using a monocular camera is not sufficient to extract the moving hand region. The loss of information in RGB images is maximum due to unpredictable background, self-occlusion, illumination variation, and erratic pattern of the hand movement [8, 10, 14].
The second component in the design of DHGR is description of the region of interest or “target modeling.” In this section, features that are repetitive, unique, and invariant to general variations, e.g., illumination, rotation, translation of the hand region are collected. These features model the target of tracking and are responsible for detecting and localizing the target in all frames of a video. This step is very significant because it helps to detect the target in an unconstrained environment [10, 12].
Li X. et al. [12] presented a very detailed study of the building blocks of visual object tracking and the associated challenges. They stated that effective modeling of the appearance of the target is the core issue for the success of a visual tracker. Practically, effective modeling is greatly affected by many factors such as target speed, illumination conditions, state of occlusion, complexity in shape, and camera stability, etc. Skin color features are the most straightforward characteristic of the hand used in the HGR domain to identify the hand region in the scene. Huang H. et al. simply detected skin color for contour extraction and then classified them using VGGNet [23]. M. H. Yao et al. [24] extracted 500 particles using the CAMShift algorithm for tracking the moving hand region. In this case, the real-time performance of the HGR system decreases when a similar color object (face or arm region) interferes. As the number of particles increases the complexity of the system increases. The HGR technique proposed by Khaled H. et al. [25] emphasized the use of both shape and skin color features for hand area detection because of background conditions, shadows, visual overlapping of the objects. They stated that noise added due to camera movement is one of the major problems in real-time hand tracking. Liu P. et al. [26] proposed a single-shot multibox detector ConvNet architecture that is like Faster R-CNN to detect hand gestures in a complex environment. Bao P. et al. [27] expressed that since the size of hand posture is very small, therefore misleading behavior or the overfitting problem becomes prominent in regular CNN.
In the method discussed in [10], we have shown that though the local representation of the hand is a comparatively more robust approach to detect the hand region, but they often suffer from background disturbance in a real-time tracking. In general, hand-crafted features result in large computations and loss of trajectory visual while tracking in real-time hand movement is very common. Henceforth, it is difficult for hand-crafted features to perfectly describe all variations in target as well as background [10, 12]. According to Shin J. et al. [28], the trackers that visually trace the object, based on appearance and position, must have a high tolerance for appearance and position. Tran D. et al. [29] initially detected the palm region from depth data collected by Kinect V2 skeletal tracker followed by morphological processing. They determined hand contour using a border tracing algorithm on binary image converted using a fixed threshold. After detecting fingertip by K-cosine algorithm, hand posture is classified using 3DCNN.
Matching of hand gesture trajectory is another important phase in the cognitive recognition of DHGR. The main constrain in generating similarity index in HGR is the speed of hand motion and the path of movement. Both these factors are highly dependent on the user’s mood and surrounding conditions at the instant of movement. Similarity matching based on distance metrics generally fails to track efficiently as hand gestures of the same meaning do not follow the same path always.
Dan Zhao et al. [30] used a hand shape fisher vector to find the movement of the finger and then classified it by linear SVM. Plouffe et al. [31] proposed Dynamic Time Wrapping (DTW) to match the similarity between target and trained gesture. In [32], a two-level speed normalization procedure is proposed using DTW and Euclidean distance-based techniques. In this method, for each test gesture, 10 best-trained gestures are selected using the DTW algorithm. Out of these 10 gestures, the most accurate gesture is selected by calculating Euclidean distance. Pablo B. et al. [33] suggested a combination of the Hidden Markov Model (HMM) and DTW, in the prediction stage.
The proposed system is designed by using a web camera; it is a simple RGB camera. The use of the RGB camera is limited in the field of hand gesture tracking because of various difficulties as discussed above (Figure 1).
Architecture of the proposed system.
The proposed system is divided into three modules:
This module is also known as the “hand detection module.” Here the posture of the hand, which is used by the user in real-time hand movement events, is detected. When the user moves his hand in front of the web camera attached to any machine acquires a video of 5–6 seconds at a rate of 15 frames per second. This video comprises a raw data sequence of length 100–150 frames; it is saved in a temporary folder, resizing all the frames to size [240, 240]. In this module, detection of an online Active Hand Template (AHT) is made using Faster Region-based Convolutional Neural Network (Faster R-CNN).
We have proposed the design of an online hand detection scheme (AHT) using Faster Region-based Convolutional Neural Network (Faster R-CNN) [34], on Residual Network (ResNet101) [35], a deep neural architecture. Three major issues that are encountered in online tracking of hand motion captured using simple cameras are as follows:
A hand is a versatile object in comparison with other objects. The area occupied in the image frame has a high variation that depends on the posture selected.
It is not fixed that the subject starts the motion from the first frame or the fixed position in the frame.
Anthropometric and scale variation in the hand are very prominently seen during hand movement in RGB images.
Thus, the essential requisite of any technique is to cope with the abovementioned factors. In the proposed method, these issues are solved by using Faster-RCNN, a Deep Neural Network (DNN) architecture. Deep learning algorithms (DLAs) are models for a machine to learn and execute any task as human beings perform. Deep networks directly learn features from raw data by exploiting local information of the target, with no manual extraction or elimination of background. Convolutional Neural Network (ConvNet) is a powerful tool in the computer vision field that mainly deals with images.
Ren S. et al. [34] modified fast RCNN to Faster Region-based Convolutional Neural Network (Faster R-CNN). They added a region proposal network (RPN) (a separate CNN network) that simultaneously estimates objectness score and regresses the boundaries of the object using the anchor box concept.
The architecture of the proposed Faster R-CNN developed on ResNet 101 is shown in Figure 2. Region Proposal Network (RPN) is an independent small-sized ConvNet, designed to directly generate region proposals from an image of any size without using a fixed edge box algorithm. The process of RPN is shown in Figure 3; here region proposals are generated from the activation feature map of the last shared convolutional layer between the RPN network and Fast-RCNN. It is implemented with an
The architecture of the proposed faster R-CNN.
Process in RPN.
Anchor boxes are bounding boxes with predefined height and width to capture the scale and aspect ratio of the target object. There are pyramids of anchors. The anchor-based method is translation invariant and detects objects of multiple scales and aspect ratios. For every tiled anchor box, the RPN predicts the probability of object, background, and intersection over union (IoU) values. The advantage of using the anchor boxes in a sliding window-based detector is to detect, encode, and classify the object in the region in a single process [34].
The design of the proposed Faster-RCNN technique is accomplished on Residual network (Resnet) resnet 101. Resnet architecture network was proposed in 2015 by Kaiming He et al. [35], to ease the learning process in a deeper network. They exhibited that a resnet architecture eight times deeper than VGG16 still has less complexity in training on ImageNet dataset. The proposed use of resnet 101 in the design of Faster R-CNN solves the complex problems in object classification by using a large number of hidden layers without increasing the training error. Furthermore, the network does not have a vanishing and exploding gradient problem because of the “skip connection” approach.
This module handles the feature extraction process of the AHT that helps in the continuous localization of the moving hand region. Our method processes a hybrid framework that combines Scale Invariant Feature Transform (SIFT) and Faster-RCNN. A framework with hybrid characteristics is selected because in real-time movement, the geometrical shape of any posture changes many times, and thus it is difficult to detect the moving hand region with only hand-crafted features i.e., SIFT. Whenever the posture is changed above the threshold (number of matched features <= 3), then AHT is determined using Faster R-CNN, and the previous AHT is updated with new AHT. During this process, a bounding box is also constructed around the centroid of the hand movement to determine the current two-dimensional area covered by the hand region.
In motion modeling, we have used SIFT algorithm designed by David Lowe [36], for local feature extraction of AHT. As compared with global features such as color, contour, texture, local features have high distinctiveness, better detection accuracy toward local image distortions, viewpoint change, and partial occlusion. Therefore, SIFT detects the object in the cluttered background without performing any segmentation or preprocessing algorithms [36, 37]. The combination of SIFT and Faster-RCNN is helpful in real-time fast-tracking of the non-rigid subtle object hand.
SIFT algorithm comprises of feature detector as well as a feature descriptor. In general, features are high-contrast areas example point, edge, or small image patch, in an image. These features are extracted such that they are detectable even in noise, scale variation, and during the change in illumination. Each SIFT feature is defined by four parameters:
In our approach, we find the SIFT features of the AHT template obtained in module-I, since it contains only the target hand posture and is small as compared with the image frame [240, 240]. Therefore, this approach saves time in matching unnecessary features and pruning them further [20, 21].
Let there be m key features in AHT frame, given as
Initially, we find the first nearest neighbors (FNN) of all the SIFT features in AHT with SIFT features in the current frame. The First Nearest Neighbors (FNNs) are defined as the pairs of key points in two different frames with a minimum sum of squared differences for the given descriptor vector
where
In the second step, matching is improved by performing Lowe’s Second Nearest Neighbor (SNN) test using Eq. (2).
SNN test is done by calculating the ratio between the FNND of
Further to find the geometrically consistent points, we apply the geometric verification test (Eq. (3)) on the key points obtained after SNN.
Here
This module deals with the cognitive recognition of the trajectory. Here the cognitive recognition means vision-based intellectual development of machine for the interpretation of hand movement. Because hand movements do not have a fixed pattern, by nature movement patterns are erratic. Due to this characteristic, till now static hand gesture recognition is more preferred than dynamic hand gesture recognition. We have determined the centroids of hand location in the tracked frames. To derive the meaning of hand movement, we have used the modified back-propagation Artificial Neural Network (m-BP-ANN) Match of test trajectory to train database. This cognitive stage is very significant for DHG because the way we collect and transform the centroid of hand movement
We have made use of the concept of the quadrant system of the Cartesian plane to transform the image frame into a 2-D plane. The two-dimensional Cartesian system divides the plane of the frame into four equal regions called Quadrants. Each quadrant is bound by two half-axes, with the center in the middle of a frame. The translation of the image frame axis to a Cartesian axis is done using Eqs. (4) and (5):
Here
Back-propagation (BP) is a supervised training procedure in feed-forward neural networks. It works on minimizing the cost function of the network using the delta rule or gradient descent method. The value of the weights with which we obtain the minimum cost function is the solution for the given learning problem. The error function
The minimization of the error function is carried out using gradient descent or delta rule. It determines the amount of weight update based on gradient direction along with step size. It is given by Eq. (7):
In the traditional BP, the optimization of the multidimensional cost function is difficult because step size is fixed, since the performance parameters are highly dependent on the learning rate
The term momentum (
In the proposed prototype, we have developed eight vision-based commands to operate and machine remotely by showing hand gestures. The proposed model of ANN has three layers, input layer, hidden layer, and output layer as shown in Figure 4. The input layer has 4 neurons, the hidden layer has 10 neurons, and the outer layer consists of 8 neurons.
Architecture of the proposed ANN model.
In this research work, we have taken three hand postures (as shown in Table 1) to demonstrate the vision-based tracking efficiency of our proposed concept. It is the unique feature of this work as most of the techniques demonstrate tracking of hand movements performed by a single posture [32]. For consolidated evaluation, we have taken approximately 100 data sequences captured in different environments as shown in Figure 5. Our database is a collection of publicly available dataset [32] and self-prepared data sequence. In [32], hand movements are mainly performed by a single hand posture (Posture III as shown in Table 1) and in a constrained laboratory environment.
Dataset for training faster R-CNN.
In self-prepared dataset, we have collected hand movements performed by six participants of three different age groups: two kids (age 10–16 years), two adults (age 20–40 years), and two seniors (age 65 years). In this, the hand movement is carried out using three different postures (as illustrated in Table 1), in linear as well as circular pattern. In self-collected dataset, 15 frames per second are taken through the web camera, and gesture length varies from 120 to 160 frames.
The evaluation of the proposed online adaptive hand tracking methodology is carried out on four test parameters. The methodology is also compared with the contemporary techniques that are based on RGB images or webcam images. The four test parameters are as follows:
Accuracy in hand detection in real-time complex images, i.e., video is captured in unconstrained background and covers natural variations occurring in geometrical contour of the postures.
Parametric evaluation of the proposed Faster R-CNN on resnet101 architecture on training and validation data.
The efficiency of a hybrid tracking system in complex environment.
Effectiveness of cognitive recognition of hand trajectory as machine command.
Figure 6 demonstrates the outcome of the hand recognition stage of different data sequences captured (using three hand postures demonstrated in Table 1) in different backgrounds under d
Various outcomes of module-I (simple background, complex background, the subject is also visible in camera range).
ifferent illumination conditions. To test the accuracy of the hand detection scheme in recognizing the hand region, we have considered nearly all possible combinations: only the hand is visible in the camera view, subject face along with arm region is in the camera view, illumination conditions are unstable, background has same color as the hand region, etc. Thus, the hand detection results in Figure 6 illustrate the following distinguishing key features of our proposed system:
Large diversity is present in hand shape and sizes also, the same posture differs in geometrical shapes and area of coverage in the image frame. Our technique does not require any foreground and background modeling. It detects the hand region by automatic learning, the discriminative deep features of the hand postures.
The subject’s state of mind at the instance of hand movement is not alike. Thus, it is not necessary that the hand is completely visible from the first frame. Our technique is not affected by the location from which the user starts their motion, it is also unaffected by the face region or other body parts of the user present in the data sequence.
The proposed hand detection module, developed on Faster R-CNN architecture, has been evaluated on the following parameters:
Accuracy: It is the parameters by which the network is evaluated and selected. It gives the count of accurate predictions.
Loss: The loss curve is the most useful diagnostic curve that accounts for variation in the predicted and actual value. Loss information helps to learn the optimization behavior of the model parameters.
Model Behavior: It talks about the learning behavior of the model. Learning pattern helps to diagnose the character of the train or validation dataset concerning the problem domain.
Root Mean Square Error (RMSE): It calculates the standard deviation between the actual value and the predicted value. The RMSE is applied in regression analysis and classification of the predicted bounding box with the ground truth bounding box. It is calculated during the training process for both train data and validation data.
Table 2 illustrates detailed performance outcomes of the proposed Faster R-CNN based on the abovementioned parameters. The observations are taken at intervals of 50, 100, 150, 200, 220 iterations. The outcomes illustrate following points of our proposed architecture on Faster R-CNN constructed on resnet101:
As the number of iterations increases, the accuracy of train data increases, and it reaches the maximum value at the 200th iteration.
Validation data achieve the maximum accuracy at 220th iterations.
There is a linear decrement in the RMSE and the loss values of both the train and validation dataset. This linear decrement reflects the stable learning behavior of the proposed model.
It is observed that at the 200th iteration, RMSE and loss of train data reached at its minimum value of 0.14 and 0.154, respectively. Similarly, in the case of the validation dataset, the value of RMSE and loss reached their minimal at the 200th iteration.
Types of postures used in the proposed system.
No. of iteration | Train data accuracy | Validation data accuracy | Train data RMSE | Validation data RMSE | Train data loss | Validation data loss |
---|---|---|---|---|---|---|
1 | 30.24 | 78.26 | 0.23 | 0.22 | 2.9331 | 2.2522 |
50 | 97.07 | 98.80 | 0.19 | 0.19 | 0.9396 | 0.6902 |
100 | 98.32 | 98.87 | 0.15 | 0.19 | 0.4791 | 0.6049 |
150 | 99.03 | 98.85 | 0.16 | 0.17 | 0.2671 | 0.5956 |
200 | 99.07 | 97.86 | 0.14 | 0.17 | 0.1544 | 0.55544 |
220 | 98.92 | 98.76 | 0.17 | 0.17 | 0.2052 | 0.6262 |
Outcomes in the training process of the proposed faster R-CNN model.
Based on above outcomes, the characteristic features of the proposed trained resnet101 are:
Accuracy: 98.76%
Loss: 0.17
The behavior of the Network: Well fit.
In this section, we have evaluated the tracking efficiency of our proposed hybrid method. The data sequences captured are of variable length ranging from 100 to 150 frames. Figure 7 shows results of tracking in different data sequences, approximately 10–12 frames of each data sequence are shown here to highlight the tracking efficiency of module II. Each frame is illustrated by its frame number, a yellow box enclosing the hand region and a yellow dot inside the yellow box represent the instant position of the centroid of the hand region. Figure 7(a) shows the tracking of P-I posture in a cluttered background. This data sequence is captured in a background that has many similar colored objects as that of the hand. Our proposed system discriminates and localizes the hand region efficiently due to the robust deep feature learning capability of our hybrid tracking system. It is also noticeable that the hand is properly identified even when the hand region was blurred due to sudden erratic movement by the subject as shown in frame 99 of the data sequence.
Tracking outcomes of different data sequence are shown in (a), (b), (c), and (d) shows the cognitive recognition of hand movement in (c).
Figure 7(b) displays the tracking results of the P-III hand posture in improper illumination conditions. It can be noticed that in Figure 7(a) and (b), the FoS are frame 3 and frame 15, respectively. This data sequence is mainly affected by the color reflection of the background wall, and thus, it is visible that the edges of the P-III posture are nearly mixed with the background in some frames.
Figure 7(c) demonstrates the tracking results of a data sequence [32] in which a teenage girl is moving her hand (posture P-III) in front of her face. It is noticeable that the hand region and face region nearly overlap in frame 17. The fast change in the hand position in the frames indicates that the subject is moving her hand in a speedy manner. The change in the distance between the two positions of the hand frame 45 to frame 59 along with the change from a clear image of the hand region to the blurred image of the hand image proves the fast movement of the hand. During the movement, the subject is also changing the orientation of the hand posture as can be seen from the frames 59, 73, 80, 87.
Cognitive efficiency means the development of the semantic between the trajectory of the dynamic hand gestures and machine command. Since, hand gestures do not follow a fixed line of movement to convey the same meaning. Therefore, syntax formation to match train data and test data is a challenge. Hence, the main limitation in DHGR is the development of a process that can convert the trajectory of hand movement to machine command. Our proposed method handles this difficult challenge in a schematic manner.
In our proposed technique, we have developed eight vision-based commands “INSTRUCTION 1–8” (abbreviated as INT-1 to INT-8). For the vision-based instruction, we have drafted a process to convert trajectory of the hand movement obtained in module-II to a machine command by using Cartesian plane system as illustrated in Figure 8.
Conversion of trajectory of hand movement to machine command.
Figure 7(d) illustrates the process in developing cognitive ability to recognize hand movement by the machine. This process consists of three steps: (i) trajectory plot of the hand movement, (ii) position of start and end point in Cartesian plane, and (iii) conversion to machine command. Figure 7(d) demonstrates the results of the cognitive recognition of a data sequence shown in Figure 7(c) [32]; here an adult girl moves her hand from right to left and the machine recognizes this movement as command 7.
Figure 9(a) shows tracking results of P-III posture performed by a teenage boy. In this data sequence, we can notice that scale change of the hand region is very prominent (as the size of the hand region is continuously changing from frame to frame). The posture area is big in frame 37, and it gradually decreases till frame 147. This indicates the distance between the subject’s hand and the camera, it is minimum in frame 37 and maximum at frame 147. Figure 9(b) displays the result of cognitive recognition of the trajectory in the three steps in trajectory to command interpretation of left initiated data sequences The movement starts from the bottom left, moves in a zigzag manner, and finally reaches close to the initial starting place. The PoS and end location of this sequence both are in the third and fourth quadrant respectively; thus, “INSTRUCTION 8” is generated through this hand movement.
Tracking results of P-III posture performed by a teenage boy. (b) Cognitive recognition of hand movement.
In this section, we compare our process and results with two different approaches used recently in the field of DHGR. In the first approach [32] technique utilizes true RGB images. This approach mainly involves hand-crafted features for hand detection and tracking. The research work conducted by Singha J. et al. [32] focused on only fist posture tracking in a fixed background, they have achieved 92.23% efficiency when no skin color object is present in the surroundings. One of the prominent limitations in their approach is that they have applied sequence of algorithms for precise detection of hand region. This method is complex and unsuitable for real-time implementation of DHGR.
In the approach proposed by Tran DS et al. [29] for fingertip tracking, depth coordinates of fingertip provided by the inbuilt software of the advanced sensor-based camera are directly used. According to the researchers, RGB camera images are largely affected by illumination variation, and thus, to avoid background and illumination complexities in DHGR, they utilized RGB-D data sequences captured through the Microsoft Kinect V2 camera. It is a skeletal tracker camera that provides the position of 25 joints of the human skeleton including fingertips. This method is designed for tracking only seven hand movements comprised of 30–45 frames in three fixed backgrounds; besides, subjects are also trained to perform correct hand movement. In this research work, each frame is allotted an individual 3DCNN for classification. Thus, the experiments can perform fingertip tracking only for short gesture length. The training time of the 3D CNN is 1 hr. 35 minute with a six-core processor of 16GB RAM, which indicates the complex architecture of the technique. The accuracy of the trained 3D CNN model is 92.6% on validation data. Table 3 illustrates and compares different technical aspect of the above two mentioned approaches with our proposed method:
Parameters | Research work-I (2018) [36] | Research work-II (2020) [29] | Proposed research work |
---|---|---|---|
Camera/Image type | Simple webcam/RGB | Microsoft Kinect Sensor version 2/depth, | Webcam/RGB |
Preprocessing | Face segmentation using ViolaJones and the background subtraction using skin filtering | Noise Removal using median filtering and morphological processing. Conversion to binary image | Not Required |
Initial stage-Hand detection | Three frames differencing on colored and grayscale images. | Hand Contour is extracted using Moore -Neighbor algorithm. Fingertip extraction using K-cosine algorithm. | Designed Faster-RCNN constructed on ResNet101. Used region-based network (RPN) for defining hand region. |
Feature Extraction | Eigen features of the detected hand region. Remove unwanted features using compact criteria | Position of Fingertip calculated through inbuilt software of the camera. | SIFT feature extraction of AHT |
Tracking methods | KLT features followed 44 features matching by compact criteria | For each frame, a 3D CNN is allotted. | Combination of Faster RCNN with SIFT algorithm. |
Classification | Results of ANN, SVM, kNN classifiers are fused to get the final classified value | Ensemble learning to generate a final probability for classification | Using ANN with Cartesian quadrant system. |
Background to conduct experiments | Fixed laboratory environment without any skin color object | Three fixed backgrounds | Any real-time background. |
Accuracy of Methodology | 92.23% | 92.60% | 95.83% |
Limitations | KLT features get reduced in subsequent frames. | (i) Preprocessing is required (ii) For each frame separate 3D CNN is required this makes the system slow. (iii) fixed gesture length of 20 frames. | Initially trained for five gestures and can be extended for many more postures |
Comparative analysis of two recent methods with the proposed methodology based on different parameters.
This research work presents solutions to many crucial and unresolved challenges in vision-based tracking of hand movement captured using a simple camera. The methodology has the potential to provide a complete solution from hand detection to tracking and finally for cognitive recognition of trajectory to machine command for contactless Human-Machine interaction via dynamic hand gestures. Since the proposed design is implemented around a single RGB webcam, thus the system is economical and user-friendly. The accuracy achieved in the online and adaptive hand detection scheme with Faster R-CNN is 98.76%. The proposed hybrid tracking scheme exhibits high efficiency to adapt scale variation, illumination variation, and background conditions. It also exhibits high accuracy when camera is in motion during the movement. The overall accuracy achieved by our proposed system in complex conditions is 95.83%.
The comparative analysis demonstrates that our system gives users the freedom to select posture and to start the hand movement from any point in the image frame. Also, we do not impose any strict conditions in terms of geometrical shape of any posture. The hybrid framework and cognitive recognition features of our proposed method give a robust solution to classify any hand trajectory in a simple manner. This feature has not been discussed in any existing technique working with RGB images till date. The cumulative command interpretation efficiency of our system in real-time environment is 96.2%. The various results justify the “online” hand detection and “adaptive” tracking feature of the proposed technique. In the future, the method can be further extended to track multiple hand movements.
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