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|a Color in computer vision :
|b fundamentals and applications /
|c Theo Gevers [and others].
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|a Hoboken, NJ :
|b Wiley,
|c ©2012.
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|a 1 online resource
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|a Includes bibliographical references and index.
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|a 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, iColor in /iiComputer Vision /iexplains:/ ulliComputer vision, including color-driven algorithms and quantitative results of various state-of-the-art methods/liliColor science topics such as color systems, color reflection mechanisms, color invariance, and color constancy/liliDigital image processing, including edge detection, feature extraction, image segmentation, and image transformations/liliSignal processing techniques for the development of both image processing and machine learning/liliRobotics 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./li/ul.
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|a Color Fundamentals -- Color Vision -- Color Image Formation -- Photometric Invariance -- Pixel-Based Photometric Invariance -- Photometric Invariance from Color Ratios -- Derivative-Based Photometric Invariance -- Photometric Invariance by Machine Learning -- Color Constancy -- Illuminant Estimation and Chromatic Adaptation -- Color Constancy Using Low-level Features -- Color Constancy Using Gamut-Based Methods -- Color Constancy Using Machine Learning -- Evaluation of Color Constancy Methods -- Color Feature Extraction -- Color Feature Description -- Color Image Segmentation -- Applications -- Color Naming -- Segmentation of Multispectral Images.
|
590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
|
650 |
|
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|a Computer vision.
|
650 |
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|a Color vision.
|
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|
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|a Color photography.
|
650 |
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|a Vision par ordinateur.
|
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|a Vision des couleurs.
|
650 |
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|a Photographie en couleurs.
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|
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|a color photography.
|2 aat
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|a COMPUTERS
|x Computer Vision & Pattern Recognition.
|2 bisacsh
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|a Color photography.
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|a Color vision.
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|a Computer vision.
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|a Gevers, Theo.
|
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|i Print version:
|t Color in computer vision.
|d Hoboken, NJ : Wiley, ©2012
|z 9780470890844
|w (DLC) 2012000650
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