دانلود کتاب 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 methodsColor science topics such as color systems, color reflection mechanisms, color invariance, and color constancyDigital image processing, including edge detection, feature extraction, image segmentation, and image transformationsSignal processing techniques for the development of both image processing and machine learningRobotics 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.Content: Chapter 1 Introduction (pages 1–9): Chapter 2 Color Vision (pages 11–25): By Marcel P. LucassenChapter 3 Color Image Formation (pages 26–45): Chapter 4 Pixel?Based Photometric Invariance (pages 47–68): Chapter 5 Photometric Invariance from Color Ratios (pages 69–80): With contributions by Cordelia SchmidChapter 6 Derivative?Based Photometric Invariance (pages 81–112): With contributions by Rein van den Boomgaard and Arnold W. M. SmeuldersChapter 7 Photometric Invariance by Machine Learning (pages 113–134): With contributions by Jose M. Alvarez and Antonio M. LopezChapter 8 Illuminant Estimation and Chromatic Adaptation (pages 135–142): Chapter 9 Color Constancy Using Low?level Features (pages 143–151): Chapter 10 Color Constancy Using Gamut?Based Methods (pages 152–160): Chapter 11 Color Constancy Using Machine Learning (pages 161–171): Chapter 12 Evaluation of Color Constancy Methods (pages 172–186): Chapter 13 Color Feature Detection (pages 187–220): With contributions by Arnold W. M. Smeulders and Andrew D. BagdanovChapter 14 Color Feature Description (pages 221–243): With contributions by Gertjan J. BurghoutsChapter 15 Color Image Segmentation (pages 244–268): With contributions by Gertjan J. BurghoutsChapter 16 Object and Scene Recognition (pages 269–286): With contributions by Koen E. A. van de Sande and Cees G. M. SnoekChapter 17 Color Naming (pages 287–317): With contributions by Robert Benavente, Maria Vanrell, Cordelia Schmid, Ramon Baldrich, Jakob Verbeek and Diane LarlusChapter 18 Segmentation of Multispectral Images (pages 318–338): With contributions by Harro M. G. Stokman