Abstract
Recently, it has been shown that morphological openings and closings can be viewed as consistent MAP estimators of (morphologically) smooth random sets immersed in clutter, or suffering from random dropouts [1]—[3]. These results hold for one-sided (union or intersection) noise. In the case of two-sided (union and intersection) noise we now know that this is no longer the case: in this more general setting, as it turns out, optimal estimators are not increasing operators [4]. Then, how may one efficiently compute an optimal random set estimate in this more general setting? For 1-D and certain restricted 2-D random set models the answer is provided by the Viterbi algorithm [5]. In the general case of 2-D random set models in two-sided noise the answer is unknown, and the task of finding it constitutes a challenging research problem.
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