Mathematical morphology and higher-order neural networks

Slawomir Skoneczny; Jaroslaw Szostakowski; Andrzej Stajniak; Witold Zydanowicz

doi:10.1117/12.216363

11 August 1995 Mathematical morphology and higher-order neural networks

Slawomir Skoneczny, Jaroslaw Szostakowski, Andrzej Stajniak, Witold Zydanowicz

Proceedings Volume 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing; (1995) https://doi.org/10.1117/12.216363
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

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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