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"Color image processing has involved much interest in the recent years. The use of color in image processing is motivated by the facts that 1) the human eyes can discern thousands of colors, and image processing is used both for human interaction and computer interpretation; 2) the color image comprises more information than the gray-level image; 3) the color features are robust to several image processing procedures (for example, to the translation and rotation of the regions of interest); 4) the color features are efficiently used in many vision tasks, including object recognition and tracking, image segmentation and retrieval, image registration etc.; 5) the color is necessary in many real life applications such as visual communications, multimedia systems, fashion and food industries, computer vision, entertainment, consumer electronics, production printing and proofing, digital photography, biometrics, digital artwork reproduction, industrial inspection, and biomedical applications. Finally, the enormous number of color images that constantly are uploaded into Internet require new approaches and challenges of big visual media creation, retrieval, processing, and applications. It also gives us new opportunities to create a number of big visual data-driven applications. Three independent quantities are used to describe any particular color; the human eyes are seen all colors as variable combinations of primary colors of red, green, and blue. Many methods of the modern color image processing are based on dealing out each primary color"--
"Color image processing has involved much interest in the recent years. The use of color in image processing is motivated by the facts that 1) the human eyes can discern thousands of colors, and image processing is used both for human interaction and computer interpretation; 2) the color image comprises more information than the gray-level image; 3) the color features are robust to several image processing procedures (for example, to the translation and rotation of the regions of interest); 4) the color features are efficiently used in many vision tasks, including object recognition and tracking, image segmentation and retrieval, image registration etc.; 5) the color is necessary in many real life applications such as visual communications, multimedia systems, fashion and food industries, computer vision, entertainment, consumer electronics, production printing and proofing, digital photography, biometrics, digital artwork reproduction, industrial inspection, and biomedical applications. Finally, the enormous number of color images that constantly are uploaded into Internet require new approaches and challenges of big visual media creation, retrieval, processing, and applications. It also gives us new opportunities to create a number of big visual data-driven applications. Three independent quantities are used to describe any particular color; the human eyes are seen all colors as variable combinations of primary colors of red, green, and blue. Many methods of the modern color image processing are based on dealing out each primary color"--
"Color image processing has involved much interest in the recent years. The use of color in image processing is motivated by the facts that 1) the human eyes can discern thousands of colors, and image processing is used both for human interaction and computer interpretation; 2) the color image comprises more information than the gray-level image; 3) the color features are robust to several image processing procedures (for example, to the translation and rotation of the regions of interest); 4) the color features are efficiently used in many vision tasks, including object recognition and tracking, image segmentation and retrieval, image registration etc.; 5) the color is necessary in many real life applications such as visual communications, multimedia systems, fashion and food industries, computer vision, entertainment, consumer electronics, production printing and proofing, digital photography, biometrics, digital artwork reproduction, industrial inspection, and biomedical applications. Finally, the enormous number of color images that constantly are uploaded into Internet require new approaches and challenges of big visual media creation, retrieval, processing, and applications. It also gives us new opportunities to create a number of big visual data-driven applications. Three independent quantities are used to describe any particular color; the human eyes are seen all colors as variable combinations of primary colors of red, green, and blue. Many methods of the modern color image processing are based on dealing out each primary color"--
This book presents a machine-generated literature overview of quaternion integral transforms from select papers published by Springer Nature, which have been organized and introduced by the book’s editor. Each chapter presents summaries of predefined themes and provides the reader with a basis for further exploration of the topic. As one of the experimental projects initiated by Springer Nature for AI book content generation, this book shows the latest developments in the field. It will be a useful reference for students and researchers who are interested in exploring the latest developments in quaternion integral transforms.
This book contains some selected papers from the International Conference on Extreme Learning Machine 2019, which was held in Yangzhou, China, December 14–16, 2019. Extreme Learning Machines (ELMs) aim to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental ‘learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. The main theme of ELM2019 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This conference provides a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance of the most recent advances of ELM.
This book aims to provide state-of-the-art knowledge in the field of Intelligence of Things to both academic and industrial readers. In particular, undergraduate, graduate, and researchers may find valuable information to drive their future research. This book is considered a reference for numerous courses such as Artificial Intelligence, Internet of Things, Intelligent Systems, and Mobile Networks. In the industrial area, this book provides information on recent studies in applying AI to IoT developments, which help to align and shorten R&D processes to introduce new classes of intelligent IoT products. This book provides a technical reference for interdisciplinary studies which utilize machine learning and IoT as tools in their fields such as constructional management, smart agriculture, Earth sciences and geo-spatial analysis, intelligent business, and digital transformation in education.
The goal of the Volume I Geometric Algebra for Computer Vision, Graphics and Neural Computing is to present a unified mathematical treatment of diverse problems in the general domain of artificial intelligence and associated fields using Clifford, or geometric, algebra. Geometric algebra provides a rich and general mathematical framework for Geometric Cybernetics in order to develop solutions, concepts and computer algorithms without losing geometric insight of the problem in question. Current mathematical subjects can be treated in an unified manner without abandoning the mathematical system of geometric algebra for instance: multilinear algebra, projective and affine geometry, calculus on manifolds, Riemann geometry, the representation of Lie algebras and Lie groups using bivector algebras and conformal geometry. By treating a wide spectrum of problems in a common language, this Volume I offers both new insights and new solutions that should be useful to scientists, and engineers working in different areas related with the development and building of intelligent machines. Each chapter is written in accessible terms accompanied by numerous examples, figures and a complementary appendix on Clifford algebras, all to clarify the theory and the crucial aspects of the application of geometric algebra to problems in graphics engineering, image processing, pattern recognition, computer vision, machine learning, neural computing and cognitive systems.
This edited survey book consists of 20 chapters showing application of Clifford algebra in quantum mechanics, field theory, spinor calculations, projective geometry, Hypercomplex algebra, function theory and crystallography. Many examples of computations performed with a variety of readily available software programs are presented in detail.
Based on the bestselling Artech House classic title, Hilbert Transforms Signal Processing, this comprehensive new resource introduces complex and hypercomplex analytic signals and their applications. Professionals find in-depth explanations of the theory of multidimensional complex and hypercomplex signals illustrated with numerous examples and followed by practical applications. The survey of chosen hypercomplex algebras and the orthants of the n-dimensional Cartesian space and single-orthant operators are explored. This book also covers topics including, the polar representation of analytic signals, quasi-analytic signals, the space-frequency of n-D complex and hypercomplex signals as well as the causality of signals.