Machine Vision: Algorithms, Architectures, and Systems contains the proceedings of the workshop ""Machine Vision: Where Are We and Where Are We Going?"" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1987 in New Brunswick, New Jersey. The papers review the state of the art of machine vision and sets directions for future research. Topics covered include ""smart sensing"" in machine vision, computer architectures for machine vision, and range image segmentation. Comprised of 14 chapters, this book opens with an overview of ""smart sensing"" strategies in machine vision and illustrates how smart sensing may fit into a general purpose vision system by implementing a flexible, modular system called Pipeline Pyramid Machine. The discussion then turns to a hierarchy of local autonomy for processor arrays, focusing on the progression from pure SIMD to complete MIMD as well as the hardware penalties that arise when autonomy is increased. The following chapters explore schemes for integrating vision modules on fine-grained machines; computer architectures for real-time machine vision systems; the application of machine vision to industrial inspection; and characteristics of technologies and social processes that are inhibiting the development and/or evolution of machine vision. Machine vision research at General Motors is also considered. The final chapter assesses future prospects for machine vision and highlights directions for research. This monograph will be a useful resource for practitioners in the fields of computer science and applied mathematics.

Machine Vision: Algorithms, Architectures, and Systems contains the proceedings of the workshop ""Machine Vision: Where Are We and Where Are We Going?"" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1987 in New Brunswick, New Jersey. The papers review the state of the art of machine vision and sets directions for future research. Topics covered include ""smart sensing"" in machine vision, computer architectures for machine vision, and range image segmentation. Comprised of 14 chapters, this book opens with an overview of ""smart sensing"" strategies in machine vision and illustrates how smart sensing may fit into a general purpose vision system by implementing a flexible, modular system called Pipeline Pyramid Machine. The discussion then turns to a hierarchy of local autonomy for processor arrays, focusing on the progression from pure SIMD to complete MIMD as well as the hardware penalties that arise when autonomy is increased. The following chapters explore schemes for integrating vision modules on fine-grained machines; computer architectures for real-time machine vision systems; the application of machine vision to industrial inspection; and characteristics of technologies and social processes that are inhibiting the development and/or evolution of machine vision. Machine vision research at General Motors is also considered. The final chapter assesses future prospects for machine vision and highlights directions for research. This monograph will be a useful resource for practitioners in the fields of computer science and applied mathematics.

Machine Vision: Algorithms, Architectures, and Systems contains the proceedings of the workshop ""Machine Vision: Where Are We and Where Are We Going?"" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1987 in New Brunswick, New Jersey. The papers review the state of the art of machine vision and sets directions for future research. Topics covered include ""smart sensing"" in machine vision, computer architectures for machine vision, and range image segmentation. Comprised of 14 chapters, this book opens with an overview of ""smart sensing"" strategies in machine vision and illustrates how smart sensing may fit into a general purpose vision system by implementing a flexible, modular system called Pipeline Pyramid Machine. The discussion then turns to a hierarchy of local autonomy for processor arrays, focusing on the progression from pure SIMD to complete MIMD as well as the hardware penalties that arise when autonomy is increased. The following chapters explore schemes for integrating vision modules on fine-grained machines; computer architectures for real-time machine vision systems; the application of machine vision to industrial inspection; and characteristics of technologies and social processes that are inhibiting the development and/or evolution of machine vision. Machine vision research at General Motors is also considered. The final chapter assesses future prospects for machine vision and highlights directions for research. This monograph will be a useful resource for practitioners in the fields of computer science and applied mathematics.