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Proceedings Paper

Bit plane decomposition and shape analysis for morphological skeletonization
Author(s): Tun-Wen Pai; John H. L. Hansen
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Paper Abstract

This paper addresses the problem of structure element selection in the context of a morphological based grayscale image communication system. The morphological skeleton representation in discrete space provides a means of lossless coding, and the coding efficiency is further improved in its minimized version by choosing a more appropriate structuring element. For an image with consistent shape distribution such as a texture pattern, a more efficient and useful skeleton representation is expected. Analysis of simulated and natural image patterns show the activated points in a morphological skeleton range between 30 and 327 points using different structuring elements. A procedure is proposed which allows for the selection of a more effective structuring element from a basis set of structuring elements. The decision process of the multiprototype pattern classification is based on the minimum-distance measurement between the chain code edge vector of object and the basis set of structuring elements. For a grayscale image communication scheme, the binary morphological skeleton transformation provides a progressive transmission framework. This framework is based on the bit plane decomposition with Gray code mapping. The progressive communication system is useful for searching image databases over a narrowband communication channel. Once the image of interest is found, the progressive communication system can provide complete knowledge of the image without loss of any information. The proposed bit plane skeleton transmission system achieves data compression rates from 2.36 to 4.28 in this study, while the original image can be reconstructed exactly using the entire set of decomposed bit planes.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131501
Show Author Affiliations
Tun-Wen Pai, Duke Univ. (United States)
John H. L. Hansen, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

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