@conference {
	title = {Optimal Morphological Filters for Discrete Random Sets under a  Union or Intersection Noise},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1992},
	month = {11/1992},
	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{\textquotedblright} 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 estimators of the signal based on the noisy observation. Then we propose a "generic{\textquotedblright} procedure for the design of optimal Morphological filters for independent union or intersection noise.},
	address = {Boston, Massachusetts},
	booktitle = {The SPIE Conference on Visual Communication \& Image Processing},
	pages = {pp. 402-413}
}
