ISBN-10:
1848213476
ISBN-13:
9781848213470
Pub. Date:
07/03/2012
Publisher:
Wiley
Digital Color Imaging / Edition 1

Digital Color Imaging / Edition 1

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Product Details

ISBN-13: 9781848213470
Publisher: Wiley
Publication date: 07/03/2012
Series: ISTE Series , #613
Pages: 352
Product dimensions: 6.00(w) x 9.20(h) x 1.00(d)

About the Author

Christine FERNANDEZ-MALOIGNE is Professor at Poitiers University, Manager of Xlim-SIC Laboratory, Chasseneuil, France.

Frédérique ROBERT-INACIO is Researcher at IM2NP and lecturer at ISEN, Toulon, France.

Ludovic MACAIRE is Full Professor at Lille 1 University,LAGIS Laboratory, Villeneuve d'Asq, France.

Table of Contents

Foreword Henri Maître xi

Chapter 1 Color Representation and Processing in Polar Color Spaces Jesús Angulo Sébastien Lefèvre Olivier Lezoray 1

1.1 Introduction 1

1.1.1 Notations used in this chapter 2

1.2 The HSI triplet 3

1.2.1 Intuitive approach: basic concepts and state of the art 3

1.2.2 Geometric approach: calculation of polar coordinates 5

1.3 Processing of hue: a variable on the unit circle 8

1.3.1 Can hue be represented as a scalar? 8

1.3.2 Ordering based on distance from a reference hue 9

1.3.3 Ordering with multiple references 11

1.3.4 Determination of reference hues 13

1.4 Color morphological filtering in the HSI space 15

1.4.1 Chromatic and achromatic top-hat transforms 16

1.4.2 Full ordering using lexicographical cascades 20

1.5 Morphological color segmentation in the HSI space 24

1.5.1 Color distances and segmentation by connective criteria 25

1.5.2 Color gradients and watershed segmentation 31

1.6 Conclusion 35

1.7 Bibliography 36

Chapter 2 Adaptive Median Color Filtering Frédérique Robert-Inacio Eric Dinet 41

2.1 Introduction 41

2.2 Noise 42

2.2.1 Sources of noise 43

2.2.2 Noise modeling 45

2.3 Nonlinear filtering 47

2.3.1 Vector methods 48

2.3.2 Median filter using bit mixing 50

2.4 Median filter: methods derived from vector methods 51

2.4.1 Vector filtering 51

2.4.2 Switching vector and peer group filters 53

2.4.3 Hybrid switching vector filter 55

2.4.4 Fuzzy filters 56

2.5 Adaptive filters 60

2.5.1 Spatially adaptive filter: generic method 60

2.5.2 Spatially adaptive median filter 62

2.6 Performance comparison 66

2.6.1 FSVF 67

2.6.2 FRF 67

2.6.3 PGF and FMPGF 68

2.6.4 IPGSVF 68

2.6.5 Vector filters and spatially adaptive median filter 69

2.7 Conclusion 71

2.8 Bibliography 72

Chapter 3 Anisotropic Diffusion PDEs for Regularization of Multichannel Images: Formalisms and Applications David Tschumperlé 75

3.1 Introduction 75

3.2 Preliminary concepts 80

3.3 Local geometry in multi-channel images 81

3.3.1 Which geometric characteristics? 81

3.3.2 Geometry estimated using a scalar characteristic 82

3.3.3 Di Zenzo multi-valued geometry 83

3.4 PDEs for multi-channel images smoothing: overview 87

3.4.1 Variational methods 88

3.4.2 Divergence PDEs 91

3.4.3 Oriented Laplacian PDEs 94

3.4.4 Trace PDEs 97

3.5 Regularization and curvature preservation 102

3.5.1 Single smoothing direction 103

3.5.2 Analogy with line integral convolutions 105

3.5.3 Extension to multi-directional smoothing 107

3.6 Numerical implementation 109

3.7 Some applications 112

3.8 Conclusion 116

3.9 Bibliography 116

Chapter 4 Linear Prediction in Spaces with Separate Achromatic and Chromatic Information Olivier Alata Imtnan Qazi Jean-Christopher Burie Christine Fernandez-Maloigne 123

4.1 Introduction 123

4.2 Complex vector 2D linear prediction 124

4.3 Spectral analysis in the IHLS and L*a*b* color spaces 129

4.3.1 Comparison of PSD estimation methods 129

4.3.2 Study of inter-channel interference associated with color space changing transformations 132

4.4 Application to segmentation of textured color images 136

4.4.1 Prediction error distribution 136

4.4.2 Label field estimation 139

4.4.3 Experiments and results 140

4.5 Conclusion 145

4.6 Bibliography 146

Chapter 5 Region Segmentation Alain Clément Laurent Busin Olivier Lezoray Ludovic Macaire 149

5.1 Introduction 149

5.2 Compact histograms 150

5.2.1 Classical multi-dimensional histogram 151

5.2.2 Compact multi-dimensional histogram 152

5.2.3 Pixel classification through compact histogram analysis 156

5.3 Spatio-colorimetric classification 158

5.3.1 Introduction 158

5.3.2 Joint analysis 158

5.3.3 Successive analysis 164

5.3.4 Conclusion 166

5.4 Segmentation by graph analysis 167

5.4.1 Graphs and color images 167

5.4.2 Semi-supervised classification using graphs 173

5.4.3 Spectral classification applied to color image segmentation 176

5.5 Evaluation of segmentation methods against a "ground truth" 181

5.6 Conclusion 186

5.7 Bibliography 187

Chapter 6 Color Texture Attributes Nicolas Vandenbroucke Oliver Alata Christèle Lecomte Alice Porebski Imtnan Qazi 193

6.1 Introduction 193

6.1.1 Concept of color texture 194

6.1.2 Color texture feature specificities 197

6.1.3 Image databases 199

6.1.4 Applications involving color texture characterization 201

6.2 Statistical features 201

6.2.1 Statistical features describing color distribution 202

6.2.2 Second-order statistical features 203

6.2.3 Higher-order statistical features 211

6.2.4 Conclusion 213

6.3 Spatio-frequential features 213

6.3.1 Gabour transform 215

6.3.2 Wavelet transform 216

6.4 Stochastic modeling 217

6.4.1 Markov fields 218

6.4.2 Linear prediction models 221

6.5 Color texture classification 223

6.5.1 Color and texture approaches 224

6.5.2 Color texture and choice of color space 226

6.5.3 Experimental results 229

6.6 Conclusion 232

6.7 Bibliography 233

Chapter 7 Photometric Color Invariants for Object Recognition Damien Muselet 241

7.1 Introduction 241

7.1.1 Object recognition 241

7.1.2 Compromise between discriminating power and invariance 244

7.1.3 Content of this chapter 245

7.2 Basic assumptions 246

7.2.1 Hypotheses on color formation 246

7.2.2 Assumptions on the reflective properties of surface elements 248

7.2.3 Assumptions on camera sensor responses 249

7.2.4 Assumptions on the characteristics of the illumination 250

7.2.5 Hypotheses of the photometric and radiometric variation model 252

7.3 Color invariant characteristics 255

7.3.1 Inter- and intra-component color ratios 256

7.3.2 Transformations based on analysis of colorimetric distributions 266

7.3.3 Invariant derivatives 268

7.4 Conclusion 280

7.5 Bibliography 280

Chapter 8 Color Key Point Detectors and Local Color Descriptors Damien Muselet Xiaohu Song 285

8.1 Introduction 285

8.2 Color key point and region detectors 286

8.2.1 Detector quality criteria 286

8.2.2 Color key points 288

8.2.3 Color key regions 293

8.2.4 Simulation of human visual system 295

8.2.5 Learning for detection 297

8.3 Local color descriptors 299

8.3.1 Concatenation of two types of descriptors 300

8.3.2 Two successive stages for image comparison 302

8.3.3 Parallel comparisons 304

8.3.4 Spatio-colorimetric descriptors 306

8.4 Conclusion 308

8.5 Bibliography 309

Chapter 9 Motion Estimation in Color Image Sequences Bertrand Augereau Jenny Benois-Pineau 317

9.1 Introduction 317

9.2 Extension of classical motion estimation techniques to color image spaces 318

9.2.1 Luminance images and optical flow 318

9.2.2 Estimation of optical flow in color spaces 319

9.3 Apparent motion and vector images 324

9.3.1 Motion and structure tensor in the scalar case 324

9.3.2 Stability of tensor spectral directions 325

9.3.3 Vector approach to optical flow 326

9.4 Conclusion 334

9.5 Bibliography 336

Appendix A Appendix to Chapter 7: Summary of Hypotheses and Color Characteristics Invariances 339

A.1 Bibliography 344

List of Authors 345

Index 349

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