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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.
Parallel Computer Vision
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.
Contents:Three-Dimensional Object Pattern Representation by Array Grammars (P S P Wang)Stochastic Puzzle Grammars (R Siromoney et al.)Parallel Recognition of High Dimensional Images (M Nivat & A Saoudi)Two-Dimensional Uniquely Parsable Isometric Array Grammars (Y Yamamoto & K Morita)Replicated Image Algorithms and Their Analyses on SIMD Machines (P J Narayanan & L S Davis)The Depth and Motion Analysis Machine (O D Faugeras et al.)Image Analysis on Massively Parallel Computers: An Architecture Point of View (A Mérigot & B Zavidovique)Parallel Algorithm for Colour Texture Generation Using the Random Neural Network Model (V Atalay & E Gelenbe)and other papers Readership: Computer scientists. keywords:
World-renowned contributors present papers concerning algorithms used on the latest generation of parallel machines (MIMD). Details key applications running the gamut from medical imaging, visualization and remote sensing to HDTV, demonstrating the large computational complexity necessary to perform these tasks.
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.
An Attempt Has Been Made To Explain The Concepts Of Computer Vision And Image Processing In A Simple Manner With The Help Of Number Of Algorithms And Live Examples. I Sincerely Hope That The Book Will Give Complete Information About Computer Vision And Image Processing To The Reader.It Not Only Serves As An Introductory Academic Text, But Also Helps Practicing Professionals To Implement Various Computer Vision And Image Processing Algorithms In Real-Time Projects.
This comprehensive work merges two of the hottest topics in computer science: parallel computing and computer graphics. Selected Topics from the Table of Contents: -Overview of Accelerated Rendering Techniques -Overview of Parallel Methods for Image Generation -Issues in Parallel Algorithm Development -Overview of Base Level Implementation -Compari