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Proceedings Paper

Computation of the medial axis skeleton at multiple complexities
Author(s): Ronald D. Chaney
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Paper Abstract

The medial axis skeleton is a thin line graph that preserves the topology of a simply connected region. The skeleton has often been cited as a useful representation for shape description, region interpretation, and object recognition. Unfortunately, the computation of the skeleton is extremely sensitive to variations in the bounding contour. Tiny perturbations in the contour often lead to spurious branches of the skeleton. In this paper, we consider a robust method for computing the medial axis skeleton across a variety of scales. The scale-space is parametric with the complexity of the bounding contour. The complexity is defined as the number of extrema of curvature in the contour. A set of curves is computed to represent the bounding contour across a variety of complexity measures. The curves possessing larger complexity measures represent greater detail than curves with smaller measures. A medial axis skeleton is computed directly from each contour. The result is a set of skeletons that represent only the gross structure of the region at coarse scales (low complexity), but represent more of the detail at fine scales (high complexity).

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131554
Show Author Affiliations
Ronald D. Chaney, AI Lab./MIT (United States)


Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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