Morphological granulometric simulation: distribution of the pattern-spectrum mean and variance for binary images with overlapping elements

Francis M. Sand; Edward R. Dougherty

doi:10.1117/12.138280

16 September 1992 Morphological granulometric simulation: distribution of the pattern-spectrum mean and variance for binary images with overlapping elements

Francis M. Sand, Edward R. Dougherty

Author Affiliations +

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138280
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Several studies have discussed how the granulometric pattern-spectrum moments can provide good texture discrimination within images. Because textural images are modeled as random processes, the moments of an image's pattern spectrum are random variables, and knowledge of their distributions is key to the classification procedure. Both exact and asymptotic discriptions of the mean and variance distributions have previously been found under the assumption that the texture elements are nonoverlapping. The present study employs computer simulations to address the situation where the elements are not disjoint. The image is generated by Monte Carlo techniques with the predefined set of primitives, openings are calculated, and the pattern spectrum is found. It is seen that the pattern-spectrum mean remains close to its theoretical distribution.

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Francis M. Sand and Edward R. Dougherty "Morphological granulometric simulation: distribution of the pattern-spectrum mean and variance for binary images with overlapping elements", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138280

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