Statistically optimized morphological filters are preferable to those traditionally selected by humans. Nevertheless, full optimization has been shown to be computationally intractable. By applying first-order knowledge to select a predetermined structuring-element library upon which to apply optimization, one can greatly reduce design computation, while at the same time producing good filters. The paper sets down a paradigm for library optimization and presents a methodology for first-order-library construction. Experimental results depicted herein illustrate the goodness of the estimations.
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