12. 2D Shape Measures for Computer Vision

  1. Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor3 and
  2. Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief3,4
  1. Paul L. Rosin1 and
  2. Joviša Žunić2

Published Online: 1 MAR 2007

DOI: 10.1002/9780470175668.ch12

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

How to Cite

Rosin, P. L. and Žunić, J. (2008) 2D Shape Measures for Computer Vision, in Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems (eds A. Nayak and I. Stojmenović), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470175668.ch12

Editor Information

  1. 3

    SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada

  2. 4

    EECE, University of Birmingham, UK

Author Information

  1. 1

    School of Computer Science, Cardiff University, Cardiff CF24 3AA, Wales, UK

  2. 2

    Department of Computer Science, University of Exeter, Harrison Building North Park Road, Exeter EX4 4QF, UK

Publication History

  1. Published Online: 1 MAR 2007
  2. Published Print: 14 FEB 2008

ISBN Information

Print ISBN: 9780470044926

Online ISBN: 9780470175668

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Keywords:

  • computer vision - 2D shape measures;
  • bounding shapes and bounding geometric primitive;
  • Fourier descriptors and shape representation

Summary

Shape is a critical element of computer vision systems, and can be used in many ways and for many applications. Examples include classification, partitioning, grouping, registration, data mining, and content based image retrieval. A variety of schemes that compute global shape measures, which can be categorized as techniques based on minimum bounding rectangles, other bounding primitives, fitted shape models, geometric moments, and Fourier descriptors are described.