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.

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.

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

An introduction to color in three-dimensional image processing and the emerging area of multi-spectral image processing The importance of color information in digital image processing is greater than ever. However, the transition from scalar to vector-valued image functions has not yet been generally covered in most textbooks. Now, Digital Color Image Processing fills this pressing need with a detailed introduction to this important topic. In four comprehensive sections, this book covers: The fundamentals and requirements for color image processing from a vector-valued viewpoint Techniques for preprocessing color images Three-dimensional scene analysis using color information, as well as the emerging area of multi-spectral imaging Applications of color image processing, presented via the examination of two case studies In addition to introducing readers to important new technologies in the field, Digital Color Image Processing also contains novel topics such as: techniques for improving three-dimensional reconstruction, three-dimensional computer vision, and emerging areas of safety and security applications in luggage inspection and video surveillance of high-security facilities. Complete with full-color illustrations and two applications chapters, Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover. It is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is also appropriate for researchers who wish to extend their knowledge in the area of color image processing.

What is Color Model A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components. When this model is associated with a precise description of how the components are to be interpreted, taking account of visual perception, the resulting set of colors is called "color space." How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color Model Chapter 2: Hue Chapter 3: Munsell Color System Chapter 4: RGB Color Spaces Chapter 5: HSL and HSV Chapter 6: Chromaticity Chapter 7: CIELAB Color Space Chapter 8: Chromatic Adaptation Chapter 9: Gamut Chapter 10: Dominant Wavelength (II) Answering the public top questions about color model. (III) Real world examples for the usage of color model in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Model.

What is Color Space A color space is a particular arrangement of colors in a given space. In conjunction with color profiling, which is supported by a variety of physical devices, it is capable of supporting repeatable representations of color, regardless of whether such representations involve an analog or a digital representation. It is possible for a color space to be arbitrary, in which case the colors that are physically realized are allocated to a set of physical color swatches that have matching color names, or it can be structured with mathematical precision. The concept of a "color space" is a helpful conceptual tool that may be utilized to gain a knowledge of the color capabilities of a certain digital file or device. In the process of attempting to replicate color on a different device, color spaces can indicate whether or not shadow/highlight detail and color saturation can be preserved, as well as the degree to which either of these aspects will be diminished. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color space Chapter 2: RGB color model Chapter 3: CMYK color model Chapter 4: RGB color spaces Chapter 5: HSL and HSV Chapter 6: Chromaticity Chapter 7: CIELAB color space Chapter 8: Gamut Chapter 9: Grayscale Chapter 10: Adobe RGB color space (II) Answering the public top questions about color space. (III) Real world examples for the usage of color space in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Space.

What is Color Matching Function The color spaces designated by the CIE in 1931 are the first quantitative relationships that have been delineated between the distributions of wavelengths in the electromagnetic visible spectrum and the colors that are physiologically seen by humans in their color vision. When it comes to color management, the mathematical relationships that define these color spaces are key tools. This is especially true when working with color inks, lighted displays, and recording devices like digital cameras. In 1931, the "Commission Internationale de l'éclairage," which is translated into English as the International Commission on Illumination, was the organization that was responsible for designing the system. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: CIE 1931 color space Chapter 2: Luminous efficiency function Chapter 3: Color balance Chapter 4: Planckian locus Chapter 5: Standard Reference Method Chapter 6: Relative luminance Chapter 7: CIECAM02 Chapter 8: Standard illuminant Chapter 9: CIE 1960 color space Chapter 10: OSA-UCS (II) Answering the public top questions about color matching function. (III) Real world examples for the usage of color matching function in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Matching Function.