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Index
Cover
Copyright
Series
Title Page
Preface
Chapter 1: Introduction
1.1 From Fundamental to Applied
1.2 Part I: Color Fundamentals
1.3 Part II: Photometric Invariance
1.4 Part III: Color Constancy
1.5 Part IV: Color Feature Extraction
1.6 Part V: Applications
1.7 Summary
Part I: Color Fundamentals
Chapter 2: Color Vision
2.1 Introduction
2.2 Stages of Color Information Processing
2.3 Chromatic Properties of the Visual System
2.4 Summary
Chapter 3: Color Image Formation
3.1 Lambertian Reflection Model
3.2 Dichromatic Reflection Model
3.3 Kubelka–Munk Model
3.4 The Diagonal Model
3.5 Color Spaces
3.6 Summary
Part II: Photometric Invariance
Chapter 4: Pixel-Based Photometric Invariance
4.1 Normalized Color Spaces
4.2 Opponent Color Spaces
4.3 The HSV Color Space
4.4 Composed Color Spaces
4.5 Noise Stability and Histogram Construction
4.6 Application: Color-Based Object Recognition
4.7 Summary
Chapter 5: Photometric Invariance from Color Ratios
5.1 Illuminant Invariant Color Ratios
5.2 Illuminant Invariant Edge Detection
5.3 Blur-Robust and Color Constant Image Description
5.4 Application: Image Retrieval Based on Color Ratios
5.5 Summary
Chapter 6: Derivative-Based Photometric Invariance
6.1 Full Photometric Invariants
6.2 Quasi-Invariants
6.3 Summary
Chapter 7: Photometric Invariance by Machine Learning
7.1 Learning from Diversified Ensembles
7.2 Temporal Ensemble Learning
7.3 Learning Color Invariants for Region Detection
7.4 Experiments
7.5 Summary
Part III: Color Constancy
Chapter 8: Illuminant Estimation and Chromatic Adaptation
8.1 Illuminant Estimation
8.2 Chromatic Adaptation
Chapter 9: Color Constancy Using Low-level Features
9.1 General Gray-World
9.2 Gray-Edge
9.3 Physics-Based Methods
9.4 Summary
Chapter 10: Color Constancy Using Gamut-Based Methods
10.1 Gamut Mapping Using Derivative Structures
10.2 Combination of Gamut Mapping Algorithms
10.3 Summary
Chapter 11: Color Constancy Using Machine Learning
11.1 Probabilistic Approaches
11.2 Combination Using Output Statistics
11.3 Combination Using Natural Image Statistics
11.4 Methods Using Semantic Information
11.5 Summary
Chapter 12: Evaluation of Color Constancy Methods
12.1 Data Sets
12.2 Performance Measures
12.3 Experiments
12.4 Summary
Part IV: Color Feature Extraction
Chapter 13: Color Feature Detection
13.1 The Color Tensor
13.2 Color Saliency
13.3 Conclusions
Chapter 14: Color Feature Description
14.1 Gaussian Derivative-Based Descriptors
14.2 Discriminative Power
14.3 Level of Invariance
14.4 Information Content
14.5 Summary
Chapter 15: Color Image Segmentation
15.1 Color Gabor Filtering
15.2 Invariant Gabor Filters Under Lambertian Reflection
15.3 Color-Based Texture Segmentation
15.4 Material Recognition Using Invariant Anisotropic Filtering
15.5 Color Invariant Codebooks and Material-Specific Adaptation
15.6 Experiments
15.7 Image Segmentation by Delaunay Triangulation
15.8 Summary
Part V: Applications
Chapter 16: Object and Scene Recognition
16.1 Diagonal Model
16.2 Color SIFT Descriptors
16.3 Object and Scene Recognition
16.4 Results
16.5 Summary
Chapter 17: Color Naming
17.1 Basic Color Terms
17.2 Color Names from Calibrated Data
17.3 Color Names from Uncalibrated Data
17.4 Experimental Results
17.5 Conclusions
Chapter 18: Segmentation of Multispectral Images
18.1 Reflection and Camera Models
18.2 Photometric Invariant Distance Measures
18.3 Error Propagation
18.4 Photometric Invariant Region Detection by Clustering
18.5 Experiments
18.6 Summary
Citation Guidelines
References
Index
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