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Morphological filter based on genetic learning

A novel method for optimal morphological filtering parameters, namely the genetic training algorithm for morphological filters (GTAMF), which adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation, is presented in this paper. Experimental results show that this method is practical, easy to extend, and improves the performances of morphological filters. The operation of a morphological filer can be divided into two basic problems that include morphological operation and structuring element (SE) selection. The rules for morphological operations are predefined so the filter's properties depend merely on the selection of SE. By means of adaptive optimizing training, structuring elements possess the shape and structural characteristics of image targets, namely some information can be obtained by SE. Morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.