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11 August 1995 Mathematical morphology and higher-order neural networks
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Abstract
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.
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Slawomir Skoneczny, Jaroslaw Szostakowski, Andrzej Stajniak, and Witold Zydanowicz "Mathematical morphology and higher-order neural networks", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); https://doi.org/10.1117/12.216363
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