A novel edge detection approach based on backtracking search optimization algorithm (BSA) clustering
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Image edge information is very important in application areas such as machine learning, image processing, stereo vision, object tracking and pattern recognition. Intensity discontinuities or sudden intensity changes in a region are indicative of the edge region in that region. Although there are many approaches to detecting edge, generally intensity discontinuities or sudden intensity changes in a region are described as edge. In this study, we proposed a Backtracking Search (BSA) clustering based edge detection approach for noisy images. Proposed approach has two stages. In first stage, the edge map is calculated using the max-min filter defined in a window. In second stage, edge map is calculated via BSA based clustering with using a cost function.
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