Color in Computer Vision

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
18.2 Photometric Invariant Distance Measures 321
18.3 Error Propagation 325
18.4 Photometric Invariant Region Detection by Clustering 328
18.5 Experiments 330
18.6 Summary 338

Citation Guidelines 339

References 341

Index 363