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Proceedings Paper

Optimal morphological filters for discrete random sets under a union or intersection noise model
Author(s): Nicholaos D. Sidiropoulos; John S. Baras; Carlos A. Berenstein
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Paper Abstract

We consider the problem of optimal binary image restoration under a union or intersection noise model. Union noise is well suited to model random clutter (obscuration), whereas intersection noise is a good model for random sampling. Our approach is random set-theoretic, i.e. digital images are viewed as realizations of a uniformly bounded discrete random set. First we provide statistical proofs of some 'folk theorems' of Morphological filtering. In particular, we prove that, under some reasonable worst-case statistical scenarios, Morphological openings, closings, unions of openings, and intersections of closings, can be viewed as MAP estimation of the signal based on the noisy observation. Then we propose a 'generic' procedure for the design of optimal Morphological filters for independent union or intersection noise.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131458
Show Author Affiliations
Nicholaos D. Sidiropoulos, Univ. of Maryland/College Park (United States)
John S. Baras, Univ. of Maryland/College Park (United States)
Carlos A. Berenstein, Univ. of Maryland/College Park (United States)


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

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