Mathematical morphology and higher-order neural networks
Sławomir Skoneczny, Jarosław Szostakowski, Andrzej Stajniak, Witold Zydanowicz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Mathematical morphology (MM) is one of the most efficient tools in advanced digital image processing. Morphological techniques have been successfully applied in such cases as: image analysis, smoothing, enhancement, edge detection, skeletonization, filtering, and segmentation (watershed algorithms). Two essential operations of MM are dilation and erosion and can be implemented in several different ways. In our paper we propose their effective implementation by using higher order neural network approach (functional-link network). The novel structure and its learning method is presented. Some other neural network methods for MM operations are shown and compared with our approach.