Detection of dominant points on an object boundary: a discontinuity approach

WM Tsang*, Pong Chi YUEN, FK Lam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

In a typical partial shape recognition scheme, each scene or model object is decomposed into a concise representation formed by a sequence of contiguous primitive features terminated by pairs of dominant points. A scene object is classified as a model object if its representations are similar. The main problem of this approach is caused by the inconsistency of the dominant point distributions as images of the same object are taken under different levels of noise interference and spatial variations. In this paper a robust dominant point detection algorithm is presented. The technique employs optimal discontinuity detectors for locating the structural nodes of an object boundary with high noise rejection and accurate localization capabilities. Members of a structural node include corners and the terminals of lines and arc segments which are unaffected by spatial variations. The algorithm has been tested with images of a set of hand tools grabbed under different scaling, camera orientations and noise interference. For the majority of cases, a similar set of dominant points is obtained for different images of the same object. The encouraging results demonstrate the feasibility of the approach and its potential as a reliable basis for object recognition.

Original languageEnglish
Pages (from-to)547-557
Number of pages11
JournalImage and Vision Computing
Volume12
Issue number9
DOIs
Publication statusPublished - Nov 1994

Scopus Subject Areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • 0 - S domain
  • discontinuities
  • optimal detectors
  • polygonal approximation
  • regression analysis

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