Share Email Print

Proceedings Paper

Mathematical morphology and higher-order neural networks
Author(s): Slawomir Skoneczny; Jaroslaw Szostakowski; Andrzej Stajniak; Witold Zydanowicz
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 11 August 1995
PDF: 8 pages
Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); doi: 10.1117/12.216363
Show Author Affiliations
Slawomir Skoneczny, Warsaw Univ. of Technology (Poland)
Jaroslaw Szostakowski, Warsaw Univ. of Technology (Poland)
Andrzej Stajniak, Warsaw Univ. of Technology (Poland)
Witold Zydanowicz, Warsaw Univ. of Technology (Poland)

Published in SPIE Proceedings Vol. 2568:
Neural, Morphological, and Stochastic Methods in Image and Signal Processing
Edward R. Dougherty; Francoise J. Preteux; Sylvia S. Shen, Editor(s)

© SPIE. Terms of Use
Back to Top