The use of maximum curvature points for the recognition of partially occluded objects

Min Hong Han, Dong Sik Jang

Research output: Contribution to journalArticle

82 Citations (Scopus)


A graph-theoretic optimization method is used to recognize partially occluded objects from a 2-D image through the use of maximal cliques and a weight matching algorithm. The vertices of an occluded object image with high curvature values are classified by the objects which are hypothesized to be involved in the occlusion. A heuristic method is also developed to further improve the computational speed. A few typical examples are given to illustrate the accuracy of the optimization model as well as the simplicity of the companion heuristic method.

Original languageEnglish
Pages (from-to)21-33
Number of pages13
JournalPattern Recognition
Issue number1-2
Publication statusPublished - 1990 Jan 1
Externally publishedYes



  • Clique
  • Occlusion
  • Polygonization
  • Weight matching

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this