Hierarchical top-down shape classification based on multiresolution skeletons

Susan Yang, Peter Scott, Cesar Bandera

Research output: Contribution to journalConference articlepeer-review


This paper describes an algorithm for hierarchical shape classification based on multiresolution skeletons, and its application to the detection and identification of objects in noisy, cluttered imagery. A pyramid of stored two dimensional templates is employed to identify the object class, its location and spatial orientation. The skeleton of the object is selected for shape representation in this paper since it is a good 2D shape descriptor and relatively robust. The morphologically computed skeleton is an implementation of the medial axis transform. A real-time recognition scheme based on Borgefors' [1] chamfer matching technique is presented which employs multiresolution top-down matching of object medial axis skeletons in a 4:1 pyramid. The proliferation of candidate points at higher resolution is controlled with a clustering scheme. In order to permit small and simply shaped objects to be discriminated from large, complex objects whose skeletons are supersets, we introduce negative match weight scores on the subset of the polygon discriminating the two templates. Results with training sets of noisy and cluttered images are presented. This scheme is shown to be capable of real-time detection and characterization of targets with good reliability in a test scenario.

Original languageEnglish (US)
Pages (from-to)889-899
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Jul 29 1993
Externally publishedYes
EventBiomedical Image Processing and Biomedical Visualization 1993 - San Jose, United States
Duration: Jan 31 1993Feb 5 1993

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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