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Parallel Computer Vision
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
This comprehensive book is primarily intended for researchers, computer vision specialists, and high-performance computing specialists who are interested in parallelizing computer vision techniques for the sake of accelerating the run-time of computer vision methods. This book covers different penalization methods on different parallel architectures such as multi-core CPUs and GPUs. It is also a valuable reference resource for researchers at all levels (e.g., undergraduate and postgraduate) who are seeking real-life examples of speeding up the computer vision methods’ run-time.
Computer vision is one of the most complex and computationally intensive problem. Like any other computationally intensive problems, parallel pro cessing has been suggested as an approach to solving the problems in com puter vision. Computer vision employs algorithms from a wide range of areas such as image and signal processing, advanced mathematics, graph theory, databases and artificial intelligence. Hence, not only are the comput ing requirements for solving vision problems tremendous but they also demand computers that are efficient to solve problems exhibiting vastly dif ferent characteristics. With recent advances in VLSI design technology, Single Instruction Multiple Data (SIMD) massively parallel computers have been proposed and built. However, such architectures have been shown to be useful for solving a very limited subset of the problems in vision. Specifically, algorithms from low level vision that involve computations closely mimicking the architec ture and require simple control and computations are suitable for massively parallel SIMD computers. An Integrated Vision System (IVS) involves com putations from low to high level vision to be executed in a systematic fashion and repeatedly. The interaction between computations and information dependent nature of the computations suggests that architectural require ments for computer vision systems can not be satisfied by massively parallel SIMD computers.
This book is concerned with the aspects of real-time, parallel computing which are specific to the analysis of digitized images including both the symbolic and semantic data derived from such images. The subjects covered encompass processing, storing, and transmitting images and image data. A variety of techniques and algorithms for the analysis and manipulation of images are explored both theoretically and in terms of implementation in hardware and software. The book is organized into four topic areas: (1) theo retical development, (2) languages for image processing, (3) new computer techniques, and (4) implementation in special purpose real-time digital systems. Computer utilization, methodology, and design for image analy sis presents special and unusual problems. One author (Nagao)* points out that, "Human perception of a scene is very complex. It has not been made clear how perception functions, what one sees in a picture, and how one understands the whole picture. It is almost certain that one carries out a very quick trial-and-error process, starting from the detection of gross prominent features and then analyzing details, using one's knowledge of the world. " Another author (Duff) makes the observation that, "It is therefore more difficult to write computer programs which deal with images than those which deal with numbers, human thinking about arithmetic being a largely conscious activity.
A multicore platform uses distributed or parallel computing in a single computer, and this can be used to assist image processing algorithms in reducing computational complexities. By implementing this novel approach, the performance of imaging, video, and vision algorithms would improve, leading the way for cost-effective devices like intelligent surveillance cameras. Multi-Core Computer Vision and Image Processing for Intelligent Applications is an essential publication outlining the future research opportunities and emerging technologies in the field of image processing, and the ways multi-core processing can further the field. This publication is ideal for policy makers, researchers, technology developers, and students of IT.
Recent research results in the area of parallel algorithms for problem solving, search, natural language parsing, and computer vision, are brought together in this book. The research reported demonstrates that substantial parallelism can be exploited in various machine intelligence and vision problems. The chapter authors are prominent researchers actively involved in the study of parallel algorithms for machine intelligence and vision. Extensive experimental studies are presented that will help the reader in assessing the usefulness of an approach to a specific problem. Intended for students and researchers actively involved in parallel algorithms design and in machine intelligence and vision, this book will serve as a valuable reference work as well as an introduction to several research directions in these areas.
The computer interpretation of visual images offers unlimited potential, with applications ranging from robotics and manufacturing to electronic sensors for aiding the blind. However, there is a huge gap between the promise of technology and what is actually possible now. In order to work effectively, computers will have to sense and analyze visual scenes in a fraction of a second, but currently it is not unusual to devote an hour of computer time to the analysis of a single image. Also, such images often have to be of highly stylized scenes to make any analysis possible. The only hope for the future lies in the use of massive parallel architectures, with perhaps thousands of processors cooperating on the task. Fortunately, the spectacular advances now being made in VLSI technology may allow such parallelism to be economically feasible. This book draws together the proceedings of a key workshop held in 1987. It presents the work of leading U.K. researchers in parallel architectures and computer vision from both industry and academia, providing a clear indication of the state of the art.