Hierarchical top-down shape classification based on multiresolution skeletons

Su-Lin Yang; Peter D. Scott; Cesar Bandera

doi:10.1117/12.148701

29 July 1993 Hierarchical top-down shape classification based on multiresolution skeletons

Su-Lin Yang, Peter D. Scott, Cesar Bandera

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148701
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

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.

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Su-Lin Yang, Peter D. Scott, and Cesar Bandera "Hierarchical top-down shape classification based on multiresolution skeletons", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148701

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KEYWORDS

Detection and tracking algorithms

Image segmentation

Image classification

Image resolution

Shape analysis

Object recognition

Classification systems

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