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Parallel Architectures and Parallel Algorithms for Integrated Vision Systems

  • Book
  • © 1990

Overview

Authors:
  1. Alok N. Choudhary

    1. Syracuse University, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Janak H. Patel

    1. University of Illinois, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 108)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction

    • Alok N. Choudhary, Janak H. Patel
    Pages 1-18

  3. Model of Computation

    • Alok N. Choudhary, Janak H. Patel
    Pages 19-36

  4. Architecture of NETRA

    • Alok N. Choudhary, Janak H. Patel
    Pages 37-53

  5. Parallel Algorithms on a Cluster

    • Alok N. Choudhary, Janak H. Patel
    Pages 55-82

  6. Inter-Cluster Communication In NETRA

    • Alok N. Choudhary, Janak H. Patel
    Pages 83-117

  7. Load Balancing and Scheduling Techniques

    • Alok N. Choudhary, Janak H. Patel
    Pages 119-142

  8. Concluding Remarks

    • Alok N. Choudhary, Janak H. Patel
    Pages 143-146

  9. Back Matter

    Pages 147-157
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Keywords

About this book

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.

Authors and Affiliations

  • Syracuse University, USA

    Alok N. Choudhary

  • University of Illinois, USA

    Janak H. Patel

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