Abstract
Abstract Morphological filters are an important class of non-linear digital signal processing and analysis filters, having found a range of applications, giving excellent results in areas such as noise reduction, edge detection and object recognition. However, design methods existing for these morphological filters tend to be computationally intractable or require some expert knowledge of mathematical morphology. This paper demonstrates how simple genetic algorithms can be employed in the search for optimum morphological filters for specific signal/image processing tasks. Some examples of applying the method to some real noise-reduction tasks are shown.
Published Version
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