Paper
1 May 1994 New descriptor for skeletons of planar shapes: the calypter
Eric Pirard, Jean-Francois Nivart
Author Affiliations +
Proceedings Volume 2180, Nonlinear Image Processing V; (1994) https://doi.org/10.1117/12.172573
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
The mathematical definition of the skeleton as the locus of centers of maximal inscribed discs is a nondigitizable one. The idea presented in this paper is to incorporate the skeleton information and the chain-code of the contour into a single descriptor by associating to each point of a contour the center and radius of the maximum inscribed disc tangent at that point. This new descriptor is called calypter. The encoding of a calypter is a three stage algorithm: (1) chain coding of the contour; (2) euclidean distance transformation, (3) climbing on the distance relief from each point of the contour towards the corresponding maximal inscribed disc center. Here we introduce an integer euclidean distance transform called the holodisc distance transform. The major interest of this holodisc transform is to confer 8-connexity to the isolevels of the generated distance relief thereby allowing a climbing algorithm to proceed step by step towards the centers of the maximal inscribed discs. The calypter has a cyclic structure delivering high speed access to the skeleton data. Its potential uses are in high speed euclidean mathematical morphology, shape processing, and analysis.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric Pirard and Jean-Francois Nivart "New descriptor for skeletons of planar shapes: the calypter", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); https://doi.org/10.1117/12.172573
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Cited by 2 scholarly publications.
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KEYWORDS
Transform theory

Nonlinear image processing

Shape analysis

Computer programming

Image processing

Binary data

Image segmentation

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