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Natural Object Recognition

  • Book
  • © 1992

Overview

Authors:
  1. Thomas M. Strat

    1. Artificial Intelligence Center, SRI International, Menlo Park, USA

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Part of the book series: Springer Series in Perception Engineering (SSPERCEPTION)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Introduction

    • Thomas M. Strat
    Pages 1-11

  3. Natural Object Recognition

    • Thomas M. Strat
    Pages 13-42

  4. A Vision System for off-Road Navigation

    • Thomas M. Strat
    Pages 43-56

  5. Context-Based Vision

    • Thomas M. Strat
    Pages 57-113

  6. Experimental Results

    • Thomas M. Strat
    Pages 115-138

  7. Conclusion

    • Thomas M. Strat
    Pages 139-148

  8. Back Matter

    Pages 149-173
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Keywords

About this book

Natural Object Recognition presents a totally new approach to the automation of scene understanding. Rather than attempting to construct highly specialized algorithms for recognizing physical objects, as is customary in modern computer vision research, the application and subsequent evaluation of large numbers of relatively straightforward image processing routines is used to recognize natural features such as trees, bushes, and rocks. The use of contextual information is the key to simplifying the problem to the extent that well understood algorithms give reliable results in ground-level, outdoor scenes.

Authors and Affiliations

  • Artificial Intelligence Center, SRI International, Menlo Park, USA

    Thomas M. Strat

Bibliographic Information

  • Book Title: Natural Object Recognition

  • Authors: Thomas M. Strat

  • Series Title: Springer Series in Perception Engineering

  • DOI: https://doi.org/10.1007/978-1-4612-2932-2

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1992

  • Softcover ISBN: 978-1-4612-7725-5Published: 20 October 2013

  • eBook ISBN: 978-1-4612-2932-2Published: 06 December 2012

  • Series ISSN: 1431-858X

  • Edition Number: 1

  • Number of Pages: XVII, 173

  • Number of Illustrations: 42 b/w illustrations

  • Topics: Artificial Intelligence, Processor Architectures

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