19 February 1988 The Digital Morphological Sampling Theorem
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Proceedings Volume 0848, Intelligent Robots and Computer Vision VI; (1988); doi: 10.1117/12.942722
Event: Advances in Intelligent Robotics Systems, 1987, Cambridge, CA, United States
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.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert M. Haralick, Xinhua Zhuang, Charlotte Lin, James Lee, "The Digital Morphological Sampling Theorem", Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942722; https://doi.org/10.1117/12.942722
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KEYWORDS
Binary data

Image filtering

Machine vision

Robots

Computer vision technology

Robot vision

Filtering (signal processing)

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