THEO GEVERS, PhD, is Professor of Computer Science in the Intelligent Systems Lab at the University of Amsterdam in the Netherlands, and CVC Full Professor at the Computer Vision Center in Barcelona, Spain.
 
ARJAN GIJSENIJ, PhD, was a postdoctoral researcher in the Intelligent Systems Lab at the University of Amsterdam, the Netherlands, while writing this book.
 
JOOST van de WEIJER, PhD, is a Ramon y Cajal Fellow at the Universitat Autònoma de Barcelona, Spain.
 
JAN-MARK GEUSEBROEK, PhD, was assistant professor in the Intelligent Systems Lab at the University of Amsterdam, the Netherlands, while writing this book.

Preface xv
 
1 Introduction 1
1.1 From Fundamental to Applied 2
1.2 Part I: Color Fundamentals 3
1.3 Part II: Photometric Invariance 3
1.4 Part III: Color Constancy 4
1.5 Part IV: Color Feature Extraction 5
1.6 Part V: Applications 7
1.7 Summary 9
 
PART I Color Fundamentals 11
 
2 Color Vision 13
2.1 Introduction 13
2.2 Stages of Color Information Processing 14
2.3 Chromatic Properties of the Visual System 18
2.4 Summary 24
 
3 Color Image Formation 26
3.1 Lambertian Reflection Model 28
3.2 Dichromatic Reflection Model 29
3.3 Kubelka-Munk Model 32
3.4 The Diagonal Model 34
3.5 Color Spaces 36
3.6 Summary 44
 
PART II Photometric Invariance 47
 
4 Pixel-Based Photometric Invariance 49
4.1 Normalized Color Spaces 50
4.2 Opponent Color Spaces 52
4.3 The HSV Color Space 52
4.4 Composed Color Spaces 53
4.5 Noise Stability and Histogram Construction 58
4.6 Application: Color-Based Object Recognition 64
4.7 Summary 68
 
5 Photometric Invariance from Color Ratios 69
5.1 Illuminant Invariant Color Ratios 71
5.2 Illuminant Invariant Edge Detection 73
5.3 Blur-Robust and Color Constant Image Description 74
5.4 Application: Image Retrieval Based on Color Ratios 77
5.5 Summary 80
 
6 Derivative-Based Photometric Invariance 81
6.1 Full Photometric Invariants 84
6.2 Quasi-Invariants 101
6.3 Summary 111
 
7 Photometric Invariance by Machine Learning 113
7.1 Learning from Diversified Ensembles 114
7.2 Temporal Ensemble Learning 119
7.3 Learning Color Invariants for Region Detection 120
7.4 Experiments 124
7.5 Summary 134
 
PART III Color Constancy 135
 
8 Illuminant Estimation and Chromatic Adaptation 137
8.1 Illuminant Estimation 139
8.2 Chromatic Adaptation 141
 
9 Color Constancy Using Low-level Features 143
9.1 General Gray-World 143
9.2 Gray-Edge 146
9.3 Physics-Based Methods 150
9.4 Summary 151
 
10 Color Constancy Using Gamut-Based Methods 152
10.1 Gamut Mapping Using Derivative Structures 155
10.2 Combination of Gamut Mapping Algorithms 157
10.3 Summary 160
 
11 Color Constancy Using Machine Learning 161
11.1 Probabilistic Approaches 161
11.2 Combination Using Output Statistics 162
11.3 Combination Using Natural Image Statistics 163
11.4 Methods Using Semantic Information 167
11.5 Summary 171
 
12 Evaluation of Color Constancy Methods 172
12.1 Data Sets 172
12.2 Performance Measures 175
12.3 Experiments 180
12.4 Summary 185
 
PART IV Color Feature Extraction 187
 
13 Color Feature Detection 189
13.1 The Color Tensor 191
13.2 Color Saliency 205
13.3 Conclusions 218
 
14 Color Feature Description 221
14.1 Gaussian Derivative-Based Descriptors 225
14.2 Discriminative Power 229
14.3 Level of Invariance 235
14.4 Information Content 236
14.5 Summary 243
 
15 Color Image Segmentation 244
15.1 Color Gabor Filtering 245
15.2 Invariant Gabor Filters Under Lambertian Reflection 247
15.3 Color-Based Texture Segmentation 247
15.4 Material Recognition Using Invariant Anisotropic Filtering 249
15.5 Color Invariant Codebooks and Material-Specific Adaptation 256
15.6 Experiments 258
15.7 Image Segmentation by Delaunay Triangulation 263
15.8 Summary 268
 
PART V Applications 269
 
16 Object and Scene Recognition 271
16.1 Diagonal Model 272
16.2 Color SIFT Descriptors 273
16.3 Object and Scene Recognition 276
16.4 Results 280
16.5 Summary 285
 
17 Color Naming 287
17.1 Basic Color Terms 288
17.3 Color Names from Uncalibrated Data 304
17.4 Experimental Results 313
17.5 Conclusions 316
 
18 Segmentation of Multispectral Images 318
18.1 Reflection and Camera Models 319<

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While 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.
 
Based 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, Color in Computer Vision explains:
* Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods
* Color science topics such as color systems, color reflection mechanisms, color invariance, and color constancy
* Digital image processing, including edge detection, feature extraction, image segmentation, and image transformations
* Signal processing techniques for the development of both image processing and machine learning
* Robotics 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.