Cover -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Chapter 1. Color Representation and Processing in Polar Color Spaces -- 1.1. Introduction -- 1.1.1. Notations used in this chapter -- 1.2. The HSI triplet -- 1.2.1. Intuitive approach: basic concepts and state of the art -- 1.2.2. Geometric approach: calculation of polar coordinates -- 1.3. Processing of hue: a variable on the unit circle -- 1.3.1. Can hue be represented as a scalar? -- 1.3.2. Ordering based on distance from a reference hue -- 1.3.3. Ordering with multiple references -- 1.3.4. Determination of reference hues -- 1.4. Color morphological filtering in the HSI space -- 1.4.1. Chromatic and achromatic top-hat transforms -- 1.4.2. Full ordering using lexicographical cascades -- 1.5. Morphological color segmentation in the HSI space -- 1.5.1. Color distances and segmentation by connective criteria -- 1.5.2. Color gradients and watershed segmentation -- 1.6. Conclusion -- 1.7. Bibliography -- Chapter 2. Adaptive Median Color Filtering -- 2.1. Introduction -- 2.2. Noise -- 2.2.1. Sources of noise -- 2.2.2. Noise modeling -- 2.3. Nonlinear filtering -- 2.3.1. Vector methods -- 2.3.2. Median filter using bit mixing -- 2.4. Median filter: methods derived from vector methods -- 2.4.1. Vector filtering -- 2.4.2. Switching vector and peer group filters -- 2.4.3. Hybrid switching vector filter -- 2.4.4. Fuzzy filters -- 2.5. Adaptive filters -- 2.5.1. Spatially adaptive filter: generic method -- 2.5.2. Spatially adaptive median filter -- 2.6. Performance comparison -- 2.6.1. FSVF -- 2.6.2. FRF -- 2.6.3. PGF and FMPGF -- 2.6.4. IPGSVF -- 2.6.5. Vector filters and spatially adaptive median filter -- 2.7. Conclusion -- 2.8. Bibliography -- Chapter 3. Anisotropic Diffusion PDEs for Regularization of Multichannel Images: Formalisms and Applications -- 3.1. Introduction.

3.2. Preliminary concepts -- 3.3. Local geometry in multi-channel images -- 3.3.1. Which geometric characteristics? -- 3.3.2. Geometry estimated using a scalar characteristic -- 3.3.3. Di Zenzo multi-valued geometry -- 3.4. PDEs for multi-channel image smoothing: overview -- 3.4.1. Variational methods -- 3.4.2. Divergence PDEs -- 3.4.3. Oriented Laplacian PDEs -- 3.4.4. Trace PDEs -- 3.5. Regularization and curvature preservation -- 3.5.1. Single smoothing direction -- 3.5.2. Analogy with line integral convolutions -- 3.5.3. Extension to multi-directional smoothing -- 3.6. Numerical implementation -- 3.7. Some applications -- 3.8. Conclusion -- 3.9. Bibliography -- Chapter 4. Linear Prediction in Spaces with Separate Achromatic and Chromatic Information -- 4.1. Introduction -- 4.2. Complex vector 2D linear prediction -- 4.3. Spectral analysis in the IHLS and L*a*b* color spaces -- 4.3.1. Comparison of PSD estimation methods -- 4.3.2. Study of inter-channel interference associated with color space changing transformations -- 4.4. Application to segmentation of textured color images -- 4.4.1. Prediction error distribution -- 4.4.2. Label field estimation -- 4.4.3. Experiments and results -- 4.5. Conclusion -- 4.6. Bibliography -- Chapter 5. Region Segmentation -- 5.1. Introduction -- 5.2. Compact histograms -- 5.2.1. Classical multi-dimensional histogram -- 5.2.2. Compact multi-dimensional histogram -- 5.2.3. Pixel classification through compact histogram analysis -- 5.3. Spatio-colorimetric classification -- 5.3.1. Introduction -- 5.3.2. Joint analysis -- 5.3.3. Successive analysis -- 5.3.4. Conclusion -- 5.4. Segmentation by graph analysis -- 5.4.1. Graphs and color images -- 5.4.2. Semi-supervised classification using graphs -- 5.4.3. Spectral classification applied to color image segmentation.

5.5. Evaluation of segmentation methods against a "ground truth" -- 5.6. Conclusion -- 5.7. Bibliography -- Chapter 6. Color Texture Attributes -- 6.1. Introduction -- 6.1.1. Concept of color texture -- 6.1.2. Color texture feature specificities -- 6.1.3. Image databases -- 6.1.4. Applications involving color texture characterization -- 6.2. Statistical features -- 6.2.1. Statistical features describing color distribution -- 6.2.2. Second-order statistical features -- 6.2.3. Higher-order statistical features -- 6.2.4. Conclusion -- 6.3. Spatio-frequential features -- 6.3.1. Gabor transform -- 6.3.2. Wavelet transform -- 6.4. Stochastic modeling -- 6.4.1. Markov fields -- 6.4.2. Linear prediction models -- 6.5. Color texture classification -- 6.5.1. Color and texture approaches -- 6.5.2. Color texture and choice of color space -- 6.5.3. Experimental results -- 6.6. Conclusion -- 6.7. Bibliography -- Chapter 7. Photometric Color Invariants for Object Recognition -- 7.1. Introduction -- 7.1.1. Object recognition -- 7.1.2. Compromise between discriminating power and invariance -- 7.1.3. Content of this chapter -- 7.2. Basic assumptions -- 7.2.1. Hypotheses on color formation -- 7.2.2. Assumptions on the reflective properties of surface elements -- 7.2.3. Assumptions on camera sensor responses -- 7.2.4. Assumptions on the characteristics of the illumination -- 7.2.5. Hypotheses of the photometric and radiometric variation model -- 7.3. Color invariant characteristics -- 7.3.1. Inter- and intra-component color ratios -- 7.3.2. Transformations based on analysis of colorimetric distributions -- 7.3.3. Invariant derivatives -- 7.4. Conclusion -- 7.5. Bibliography -- Chapter 8. Color Key Point Detectors and Local Color Descriptors -- 8.1. Introduction -- 8.2. Color key point and region detectors -- 8.2.1. Detector quality criteria -- 8.2.2. Color key points.

8.2.3. Color key regions -- 8.2.4. Simulation of human visual system -- 8.2.5. Learning for detection -- 8.3. Local color descriptors -- 8.3.1. Concatenation of two types of descriptors -- 8.3.2. Two successive stages for image comparison -- 8.3.3. Parallel comparisons -- 8.3.4. Spatio-colorimetric descriptors -- 8.4. Conclusion -- 8.5. Bibliography -- Chapter 9. Motion Estimation in Color Image Sequences -- 9.1. Introduction -- 9.2. Extension of classical motion estimation techniques to color image spaces -- 9.2.1. Luminance images and optical flow -- 9.2.2. Estimation of optical flow in color spaces -- 9.3. Apparent motion and vector images -- 9.3.1. Motion and structure tensor in the scalar case -- 9.3.2. Stability of tensor spectral directions -- 9.3.3. Vector approach to optical flow -- 9.4. Conclusion -- 9.5. Bibliography -- Appendix A. Appendix to Chapter 7: Summary of Hypotheses and Color Characteristic Invariances -- A.1. Bibliography -- List of Authors -- Index.