Near-optimal mst-based shape description using genetic algorithm

Sven Loncaric, Atam P. Dhawan

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

A new method for the selection of the optimal structuring element for shape description and matching based on the morphological signature transform (MST) is presented in this paper. For a given class of shapes the optimal structuring element for MST method is selected by means of a genetic algorithm. The optimization criteria is formulated to enable a robust shape matching. Experiments have been performed on a class of model shapes. The proposed optimal shape description method is applied to the problem of shape matching which evolves in many object recognition applications. Here, an unknown object is matched to a set of known objects in order to classify it into one of finite number of classes. Experimental results are presented and discussed.

Original languageEnglish (US)
Pages (from-to)571-579
Number of pages9
JournalPattern Recognition
Volume28
Issue number4
DOIs
StatePublished - Apr 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Genetic algorithms
  • Mathematical morphology
  • Multiresolution pyramid
  • Shape description
  • Shape matching Multiscale representation

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