The use of first-order structuring-element libraries to design morphological filters

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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