Minimized morphological skeleton based on structuring-element shape analysis

Tun-Wen Pai; John H. L. Hansen

doi:10.1117/12.130871

16 December 1992 Minimized morphological skeleton based on structuring-element shape analysis

Tun-Wen Pai, John H. L. Hansen

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130871
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

The digital morphological skeleton representation provides a means of improving lossless coding in a communication system. This is due to the observation that the entropy of a morphological skeleton is less than its original image. One way to improve coding efficiency is to minimize the morphological skeleton representation 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 to 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 is based on the minimum-distance measurement in a multiprototype pattern classification. The structuring element for morphological skeletonization is from the closest match between the chain code edge vector and the basis set of structuring elements. The proposed procedure represents an organized means for choosing a more meaningful structuring element for morphological analysis. It is shown that a significant reduction in the number of required activated skeleton points will result for morphological skeletonization.

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Tun-Wen Pai and John H. L. Hansen "Minimized morphological skeleton based on structuring-element shape analysis", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130871

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KEYWORDS

Reconstruction algorithms

Image compression

Image processing

Shape analysis

Signal processing

Stochastic processes

Binary data

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