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The Structure and Properties of Color Spaces and the Representation of Color Images

Author: Eric Dubois

Publisher: Morgan & Claypool Publishers

Published: 2010

Total Pages: 130

ISBN-13: 1598292323

This lecture describes the author's approach to the representation of color spaces and their use for color image processing. The lecture starts with a precise formulation of the space of physical stimuli (light). The model includes both continuous spectra and monochromatic spectra in the form of Dirac deltas. The spectral densities are considered to be functions of a continuous wavelength variable. This leads into the formulation of color space as a three-dimensional vector space, with all the associated structure. The approach is to start with the axioms of color matching for normal human viewers, often called Grassmann's laws, and developing the resulting vector space formulation. However, once the essential defining element of this vector space is identified, it can be extended to other color spaces, perhaps for different creatures and devices, and dimensions other than three. The CIE spaces are presented as main examples of color spaces. Many properties of the color space are examined. Once the vector space formulation is established, various useful decompositions of the space can be established. The first such decomposition is based on luminance, a measure of the relative brightness of a color. This leads to a direct-sum decomposition of color space where a two-dimensional subspace identifies the chromatic attribute, and a third coordinate provides the luminance. A different decomposition involving a projective space of chromaticity classes is then presented. Finally, it is shown how the three types of color deficiencies present in some groups of humans leads to a direct-sum decomposition of three one-dimensional subspaces that are associated with the three types of cone photoreceptors in the human retina. Next, a few specific linear and nonlinear color representations are presented. The color spaces of two digital cameras are also described. Then the issue of transformations between \emph{different} color spaces is addressed. Finally, these ideas are applied to signal and system theory for color images. This is done using a vector signal approach where a general linear system is represented by a three-by-three system matrix. The formulation is applied to both continuous and discrete space images, and specific problems in color filter array sampling and displays are presented for illustration. The book is mainly targeted to researchers and graduate students in fields of signal processing related to any aspect of color imaging. Table of Contents: Introduction / Light: The Physical Color Stimulus / The Color Vector Space / Subspaces and Decompositions of the Human Color Space / Various Color Spaces, Representations, and Transformations / Signals and Systems Theory / Concluding Remarks

The Structure and Properties of Color Spaces and the Representation of Color Images

Author: Eric Dubois

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 111

ISBN-13: 3031022467

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This lecture describes the author's approach to the representation of color spaces and their use for color image processing. The lecture starts with a precise formulation of the space of physical stimuli (light). The model includes both continuous spectra and monochromatic spectra in the form of Dirac deltas. The spectral densities are considered to be functions of a continuous wavelength variable. This leads into the formulation of color space as a three-dimensional vector space, with all the associated structure. The approach is to start with the axioms of color matching for normal human viewers, often called Grassmann's laws, and developing the resulting vector space formulation. However, once the essential defining element of this vector space is identified, it can be extended to other color spaces, perhaps for different creatures and devices, and dimensions other than three. The CIE spaces are presented as main examples of color spaces. Many properties of the color space are examined. Once the vector space formulation is established, various useful decompositions of the space can be established. The first such decomposition is based on luminance, a measure of the relative brightness of a color. This leads to a direct-sum decomposition of color space where a two-dimensional subspace identifies the chromatic attribute, and a third coordinate provides the luminance. A different decomposition involving a projective space of chromaticity classes is then presented. Finally, it is shown how the three types of color deficiencies present in some groups of humans leads to a direct-sum decomposition of three one-dimensional subspaces that are associated with the three types of cone photoreceptors in the human retina. Next, a few specific linear and nonlinear color representations are presented. The color spaces of two digital cameras are also described. Then the issue of transformations between different color spaces is addressed. Finally, these ideas are applied to signal and system theory for color images. This is done using a vector signal approach where a general linear system is represented by a three-by-three system matrix. The formulation is applied to both continuous and discrete space images, and specific problems in color filter array sampling and displays are presented for illustration. The book is mainly targeted to researchers and graduate students in fields of signal processing related to any aspect of color imaging.

Multidimensional Signal, Image, and Video Processing and Coding

Author: John W. Woods

Publisher: Academic Press

Published: 2011-05-31

Total Pages: 617

ISBN-13: 0123814219

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Multidimensional Signal, Image, and Video Processing and Coding gives a concise introduction to both image and video processing, providing a balanced coverage between theory, applications and standards. It gives an introduction to both 2-D and 3-D signal processing theory, supported by an introduction to random processes and some essential results from information theory, providing the necessary foundation for a full understanding of the image and video processing concepts that follow. A significant new feature is the explanation of practical network coding methods for image and video transmission. There is also coverage of new approaches such as: super-resolution methods, non-local processing, and directional transforms. Multidimensional Signal, Image, and Video Processing and Coding also has on-line support that contains many short MATLAB programs that complement examples and exercises on multidimensional signal, image, and video processing. There are numerous short video clips showing applications in video processing and coding, plus a copy of the vidview video player for playing .yuv video files on a Windows PC and an illustration of the effect of packet loss on H.264/AVC coded bitstreams. New to this edition: New appendices on random processes, information theory New coverage of image analysis – edge detection, linking, clustering, and segmentation Expanded coverage on image sensing and perception, including color spaces Now summarizes the new MPEG coding standards: scalable video coding (SVC) and multiview video coding (MVC), in addition to coverage of H.264/AVC Updated video processing material including new example on scalable video coding and more material on object- and region-based video coding More on video coding for networks including practical network coding (PNC), highlighting the significant advantages of PNC for both video downloading and streaming New coverage of super-resolution methods for image and video Only R&D level tutorial that gives an integrated treatment of image and video processing - topics that are interconnected New chapters on introductory random processes, information theory, and image enhancement and analysis Coverage and discussion of the latest standards in video coding: H.264/AVC and the new scalable video standard (SVC)

Image Understanding using Sparse Representations

Author: Jayaraman J. Thiagarajan

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 115

ISBN-13: 3031022505

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Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.

Foundations of Colour Science

Author: Alexander D. Logvinenko

Publisher: John Wiley & Sons

Published: 2022-09-26

Total Pages: 564

ISBN-13: 1119885914

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Presents the science of colour from new perspectives and outlines results obtained from the authors’ work in the mathematical theory of colour This innovative volume summarizes existing knowledge in the field, attempting to present as much data as possible about colour, accumulated in various branches of science (physics, phychophysics, colorimetry, physiology) from a unified theoretical position. Written by a colour specialist and a professional mathematician, the book offers a new theoretical framework based on functional analysis and convex analysis. Employing these branches of mathematics, instead of more conventional linear algebra, allows them to provide the knowledge required for developing techniques to measure colour appearance to the standards adopted in colorimetric measurements. The authors describe the mathematics in a language that is understandable for colour specialists and include a detailed overview of all chapters to help readers not familiar with colour science. Divided into two parts, the book first covers various key aspects of light colour, such as colour stimulus space, colour mechanisms, colour detection and discrimination, light-colour perception typology, and light metamerism. The second part focuses on object colour, featuring detailed coverage of object-colour perception in single- and multiple-illuminant scenes, object-colour solid, colour constancy, metamer mismatching, object-colour indeterminacy and more. Throughout the book, the authors combine differential geometry and topology with the scientific principles on which colour measurement and specification are currently based and applied in industrial applications. Presents a unique compilation of the author’s substantial contributions to colour science Offers a new approach to colour perception and measurement, developing the theoretical framework used in colorimetry Bridges the gap between colour engineering and a coherent mathematical theory of colour Outlines mathematical foundations applicable to the colour vision of humans and animals as well as technologies equipped with artificial photosensors Contains algorithms for solving various problems in colour science, such as the mathematical problem of describing metameric lights Formulates all results to be accessible to non-mathematicians and colour specialists Foundations of Colour Science: From Colorimetry to Perception is an invaluable resource for academics, researchers, industry professionals and undergraduate and graduate students with interest in a mathematical approach to the science of colour.

Color Space

Author: Fouad Sabry

Publisher: One Billion Knowledgeable

Published: 2024-05-10

Total Pages: 117

ISBN-13:

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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.

Image Fusion in Remote Sensing

Author: Arian Azarang

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 89

ISBN-13: 3031022564

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Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing.

Combating Bad Weather Part II

Author: Sudipta Mukhopadhyay

Publisher: Morgan & Claypool Publishers

Published: 2015-01-01

Total Pages: 86

ISBN-13: 1627055878

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Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Fog / Dataset and Performance Metrics / Important Fog Removal Algorithms / Single-Image Fog Removal Using an Anisotropic Diffusion / Video Fog Removal Framework Using an Uncalibrated Single Camera System / Conclusions and Future Directions / Bibliography / Authors' Biographies

Dictionary Learning in Visual Computing

Author: Qiang Zhang

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 133

ISBN-13: 303102253X

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The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

Multimodal Learning toward Micro-Video Understanding

Author: Liqiang Nie

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 170

ISBN-13: 3031022556

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Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding. Micro-video understanding is, however, non-trivial due to the following challenges: (1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis? These challenges have been largely unexplored to date. In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding.