Color in Computer Vision: Fundamentals and Applications

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.

Table of Contents

1 Introduction 1

PART I Color Fundamentals 11
2 Color Vision 13
3 Color Image Formation 26

PART II Photometric Invariance 47
4 Pixel-Based Photometric Invariance 49
5 Photometric Invariance from Color Ratios 69
6 Derivative-Based Photometric Invariance 81
7 Photometric Invariance by Machine Learning 113

PART III Color Constancy 135
8 Illuminant Estimation and Chromatic Adaptation 137
9 Color Constancy Using Low-level Features 143
10 Color Constancy Using Gamut-Based Methods 152
11 Color Constancy Using Machine Learning 161
12 Evaluation of Color Constancy Methods 172

PART IV Color Feature Extraction 187
13 Color Feature Detection 189
14 Color Feature Description 221
15 Color Image Segmentation 244

PART V Applications 269
16 Object and Scene Recognition 271
17 Color Naming 287
18 Segmentation of Multispectral Images 318