Skeletonization via vertices of morphologically decomposed subsets

Dongming Zhao; Xintong Zhang

doi:10.1117/12.205238

28 March 1995 Skeletonization via vertices of morphologically decomposed subsets

Dongming Zhao, Xintong Zhang

Proceedings Volume 2424, Nonlinear Image Processing VI; (1995) https://doi.org/10.1117/12.205238
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

This paper shows that images can be decomposed into a series of homotopic subsets by means of morphological erosions using a series of disk-like structuring elements, and the skeleton can be obtained from the homotopic subset by detecting the vertices of each homotopic subset. It is an affine transform to map objects into a series of subsets and the skeleton points can be obtained from the mapped subsets individually. When a digital disk is rotation-invariant, the mapping is rotation-invariant. Consequently, the skeleton is rotation-invariant. It is shown that the convex vertices of an object of which curvatures change significantly are the skeleton points. Two algorithms for detecting vertices are presented in this paper. A fast mapping algorithm and a reconstruction algorithm are presented. Compared to other morphological methods, this proposed skeletonization method generates more accurate skeletons, particularly in the cases involving rotated shapes. Based on the skeleton, we introduce a new concept of major points (MPs) for skeleton descriptions. This is a skeleton sampling method. MPs can be obtained through choosing skeleton points with maximally weighted self-information. The MPs emphasize the contribution of each skeleton point to original objects. This paper also presents a detailed description on selections of MPs, where an object can be partially reconstructed via MPs based on a proposed reconstruction criterion.

Citation Download Citation

Dongming Zhao and Xintong Zhang "Skeletonization via vertices of morphologically decomposed subsets", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); https://doi.org/10.1117/12.205238

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KEYWORDS

Reconstruction algorithms

Error analysis

Algorithm development

Neodymium

Detection and tracking algorithms

Image compression

Intelligence systems

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