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Proceedings Paper

The Digital Morphological Sampling Theorem
Author(s): Robert M. Haralick; Xinhua Zhuang; Charlotte Lin; James Lee
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Paper Abstract

There are potential industrial applications for any methodology which inherently reduces processing time and cost and yet produces results sufficiently close to the result of full processing. It is for this reason that a morphological sampling theorem is important. The morphological sampling theorem described in this paper states: (1) how a digital image must be morphologically filtered before sampling in order to preserve the relevant information after sampling; (2) to what precision an appropriately morphologically filtered image can be reconstructed after sampling; and (3) the relationship between morphologically operating before sampling and the more computationally efficient scheme of morphologically operating on the sampled image with a sampled structuring element. The digital sampling theorem is developed first for the case of binary morphology and then it is extended to gray scale morphology through the use of the umbra homomorphism theorems.

Paper Details

Date Published: 19 February 1988
PDF: 17 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942722
Show Author Affiliations
Robert M. Haralick, University of Washington (United States)
Xinhua Zhuang, University of Washington (United States)
Charlotte Lin, Boeing High Technology Center (United States)
James Lee, Boeing High Technology Center (United States)

Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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