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Proceedings Paper

Hierarchical top-down shape classification based on multiresolution skeletons
Author(s): Su-Lin Yang; Peter D. Scott; Cesar Bandera
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Paper Abstract

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' 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.

Paper Details

Date Published: 29 July 1993
PDF: 11 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148701
Show Author Affiliations
Su-Lin Yang, SUNY/Buffalo (United States)
Peter D. Scott, SUNY/Buffalo (United States)
Cesar Bandera, Amherst Systems Inc. (United States)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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