Color In Computer Vision

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.

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.

The author presents a vision model that uses color information to interpret the effects of shading and highlights on a scene. Transcending more traditional approaches, this method may lead to more reliable and useful techniques for image understanding.

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.

Color Image Processing: Methods and Applications embraces two decades of extraordinary growth in the technologies and applications for color image processing. The book offers comprehensive coverage of state-of-the-art systems, processing techniques, and emerging applications of digital color imaging. To elucidate the significant progress in specialized areas, the editors invited renowned authorities to address specific research challenges and recent trends in their area of expertise. The book begins by focusing on color fundamentals, including color management, gamut mapping, and color constancy. The remaining chapters detail the latest techniques and approaches to contemporary and traditional color image processing and analysis for a broad spectrum of sophisticated applications, including: Vector and semantic processing Secure imaging Object recognition and feature detection Facial and retinal image analysis Digital camera image processing Spectral and superresolution imaging Image and video colorization Virtual restoration of artwork Video shot segmentation and surveillance Color Image Processing: Methods and Applications is a versatile resource that can be used as a graduate textbook or as stand-alone reference for the design and the implementation of various image and video processing tasks for cutting-edge applications. This book is part of the Digital Imaging and Computer Vision series.

What is Color Profile A set of data that, according to the standards that have been adopted by the International Color Consortium (ICC), characterizes a color input or output device or a color space is referred to as an ICC profile. This profile is the basis for color management. By providing a mapping between the device source or target color space and a profile connection space (PCS), profiles are able to provide a description of the color characteristics that are associated with a certain device or viewing requirement. Either CIELAB (L*a*b*) or CIEXYZ is considered to be this PCS. There are two ways to specify mappings: either through the use of tables, which are then subjected to interpolation, or by a sequence of parameters concerning transformations. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: ICC profile Chapter 2: Color management Chapter 3: RGB color model Chapter 4: CMYK color model Chapter 5: CIELAB color space Chapter 6: Adobe RGB color space Chapter 7: Color space Chapter 8: Prepress Chapter 9: JPEG File Interchange Format Chapter 10: TIFF (II) Answering the public top questions about color profile. (III) Real world examples for the usage of color profile 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 Profile.