Abstract
In this paper, we introduce a method to design gray scale composite morphological operators as fuzzy neural networks. In this structure, synaptic weights are represented by a gray scale structuring element. The proposed method is a two-step procedure. First, a suitable neural topology is found through the basis functions of the composite operators. Second, a learning rule based on the average least mean square is applied where each synaptic weight is found through a back propagation algorithm. One dimensional examples will be shown. This scheme can be easily extended to two dimensions.
Original language | English |
---|---|
Pages (from-to) | 280-290 |
Number of pages | 11 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 1902 |
DOIs | |
Publication status | Published - 1993 May 21 |
Event | Nonlinear Image Processing IV 1993 - San Jose, United States Duration: 1993 Jan 31 → 1993 Feb 5 |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering