Chapter 12. 2D Shape Measures for Computer Vision
- Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor3,
- Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief3,4
Published Online: 1 MAR 2007
DOI: 10.1002/9780470175668.ch12
Copyright © 2008 John Wiley & Sons, Inc.
Book Title
Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems
Additional Information
How to Cite
Rosin, P. L. and Žunić, J. (2007) 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
- 3
SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada
- 4
EECE, University of Birmingham, UK
Publication History
- Published Online: 1 MAR 2007
- Published Print: 14 FEB 2008
ISBN Information
Print ISBN: 9780470044926
Online ISBN: 9780470175668
- Summary
- Chapter
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.