{"product_id":"color-in-computer-vision-fundamentals-and-applications-hardback-9780470890844","title":"Color in Computer Vision; Fundamentals and Applications (Hardback) 9780470890844","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eColor in Computer Vision\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFundamentals and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eTheo Gevers (Author), Arjan Gijsenij (Author), Joost van de Weijer (Author), Jan-Mark Geusebroek (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470890844, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 5 October 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e24.1 x 16.1 x 2 cm, 0.789 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eWhile the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding.\u003c\/p\u003e \u003cp\u003eBased on the authors’ intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, \u003ci\u003eColor in\u003c\/i\u003e \u003ci\u003eComputer Vision\u003c\/i\u003e explains:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eComputer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods\u003c\/li\u003e \u003cli\u003eColor science topics such as color systems, color reflection mechanisms, color invariance, and color constancy\u003c\/li\u003e \u003cli\u003eDigital image processing, including edge detection, feature extraction, image segmentation, and image transformations\u003c\/li\u003e \u003cli\u003eSignal processing techniques for the development of both image processing and machine learning\u003c\/li\u003e \u003cli\u003eRobotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003cbr\u003e 1.1 From Fundamental to Applied 2\u003cbr\u003e 1.2 Part I: Color Fundamentals 3\u003cbr\u003e 1.3 Part II: Photometric Invariance 3\u003cbr\u003e 1.4 Part III: Color Constancy 4\u003cbr\u003e 1.5 Part IV: Color Feature Extraction 5\u003cbr\u003e 1.6 Part V: Applications 7\u003cbr\u003e 1.7 Summary 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I Color Fundamentals 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Color Vision 13\u003c\/b\u003e\u003cbr\u003e 2.1 Introduction 13\u003cbr\u003e 2.2 Stages of Color Information Processing 14\u003cbr\u003e 2.3 Chromatic Properties of the Visual System 18\u003cbr\u003e 2.4 Summary 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Color Image Formation 26\u003c\/b\u003e\u003cbr\u003e 3.1 Lambertian Reflection Model 28\u003cbr\u003e 3.2 Dichromatic Reflection Model 29\u003cbr\u003e 3.3 Kubelka–Munk Model 32\u003cbr\u003e 3.4 The Diagonal Model 34\u003cbr\u003e 3.5 Color Spaces 36\u003cbr\u003e 3.6 Summary 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II Photometric Invariance 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Pixel-Based Photometric Invariance 49\u003c\/b\u003e\u003cbr\u003e 4.1 Normalized Color Spaces 50\u003cbr\u003e 4.2 Opponent Color Spaces 52\u003cbr\u003e 4.3 The HSV Color Space 52\u003cbr\u003e 4.4 Composed Color Spaces 53\u003cbr\u003e 4.5 Noise Stability and Histogram Construction 58\u003cbr\u003e 4.6 Application: Color-Based Object Recognition 64\u003cbr\u003e 4.7 Summary 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Photometric Invariance from Color Ratios 69\u003c\/b\u003e\u003cbr\u003e 5.1 Illuminant Invariant Color Ratios 71\u003cbr\u003e 5.2 Illuminant Invariant Edge Detection 73\u003cbr\u003e 5.3 Blur-Robust and Color Constant Image Description 74\u003cbr\u003e 5.4 Application: Image Retrieval Based on Color Ratios 77\u003cbr\u003e 5.5 Summary 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Derivative-Based Photometric Invariance 81\u003c\/b\u003e\u003cbr\u003e 6.1 Full Photometric Invariants 84\u003cbr\u003e 6.2 Quasi-Invariants 101\u003cbr\u003e 6.3 Summary 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Photometric Invariance by Machine Learning 113\u003c\/b\u003e\u003cbr\u003e 7.1 Learning from Diversified Ensembles 114\u003cbr\u003e 7.2 Temporal Ensemble Learning 119\u003cbr\u003e 7.3 Learning Color Invariants for Region Detection 120\u003cbr\u003e 7.4 Experiments 124\u003cbr\u003e 7.5 Summary 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III Color Constancy 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Illuminant Estimation and Chromatic Adaptation 137\u003c\/b\u003e\u003cbr\u003e 8.1 Illuminant Estimation 139\u003cbr\u003e 8.2 Chromatic Adaptation 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Color Constancy Using Low-level Features 143\u003c\/b\u003e\u003cbr\u003e 9.1 General Gray-World 143\u003cbr\u003e 9.2 Gray-Edge 146\u003cbr\u003e 9.3 Physics-Based Methods 150\u003cbr\u003e 9.4 Summary 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Color Constancy Using Gamut-Based Methods 152\u003c\/b\u003e\u003cbr\u003e 10.1 Gamut Mapping Using Derivative Structures 155\u003cbr\u003e 10.2 Combination of Gamut Mapping Algorithms 157\u003cbr\u003e 10.3 Summary 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Color Constancy Using Machine Learning 161\u003c\/b\u003e\u003cbr\u003e 11.1 Probabilistic Approaches 161\u003cbr\u003e 11.2 Combination Using Output Statistics 162\u003cbr\u003e 11.3 Combination Using Natural Image Statistics 163\u003cbr\u003e 11.4 Methods Using Semantic Information 167\u003cbr\u003e 11.5 Summary 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Evaluation of Color Constancy Methods 172\u003c\/b\u003e\u003cbr\u003e 12.1 Data Sets 172\u003cbr\u003e 12.2 Performance Measures 175\u003cbr\u003e 12.3 Experiments 180\u003cbr\u003e 12.4 Summary 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV Color Feature Extraction 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Color Feature Detection 189\u003c\/b\u003e\u003cbr\u003e 13.1 The Color Tensor 191\u003cbr\u003e 13.2 Color Saliency 205\u003cbr\u003e 13.3 Conclusions 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Color Feature Description 221\u003c\/b\u003e\u003cbr\u003e 14.1 Gaussian Derivative-Based Descriptors 225\u003cbr\u003e 14.2 Discriminative Power 229\u003cbr\u003e 14.3 Level of Invariance 235\u003cbr\u003e 14.4 Information Content 236\u003cbr\u003e 14.5 Summary 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Color Image Segmentation 244\u003c\/b\u003e\u003cbr\u003e 15.1 Color Gabor Filtering 245\u003cbr\u003e 15.2 Invariant Gabor Filters Under Lambertian Reflection 247\u003cbr\u003e 15.3 Color-Based Texture Segmentation 247\u003cbr\u003e 15.4 Material Recognition Using Invariant Anisotropic Filtering 249\u003cbr\u003e 15.5 Color Invariant Codebooks and Material-Specific Adaptation 256\u003cbr\u003e 15.6 Experiments 258\u003cbr\u003e 15.7 Image Segmentation by Delaunay Triangulation 263\u003cbr\u003e 15.8 Summary 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V Applications 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Object and Scene Recognition 271\u003c\/b\u003e\u003cbr\u003e 16.1 Diagonal Model 272\u003cbr\u003e 16.2 Color SIFT Descriptors 273\u003cbr\u003e 16.3 Object and Scene Recognition 276\u003cbr\u003e 16.4 Results 280\u003cbr\u003e 16.5 Summary 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Color Naming 287\u003c\/b\u003e\u003cbr\u003e 17.1 Basic Color Terms 288\u003cbr\u003e 17.3 Color Names from Uncalibrated Data 304\u003cbr\u003e 17.4 Experimental Results 313\u003cbr\u003e 17.5 Conclusions 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Segmentation of Multispectral Images 318\u003c\/b\u003e\u003cbr\u003e 18.1 Reflection and Camera Models 319\u003cbr\u003e 18.2 Photometric Invariant Distance Measures 321\u003cbr\u003e 18.3 Error Propagation 325\u003cbr\u003e 18.4 Photometric Invariant Region Detection by Clustering 328\u003cbr\u003e 18.5 Experiments 330\u003cbr\u003e 18.6 Summary 338\u003c\/p\u003e \u003cp\u003eCitation Guidelines 339\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003eIndex 363\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" 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