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Proceedings Paper

Neuro-fuzzy image processing: relevance and feasibility
Author(s): Sankar K. Pal
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Paper Abstract

A brief review of the applications of neural networks in various aspects of Image Processing (e.g., image segmentation, image restoration, texture segmentation, image coding etc.) along with their parallelism and fault tolerance characteristics will be provided first. Relevance of various fuzzy tools and methodologies for handling uncertainties and in providing soft decisions in image processing will be described. A discussion on making fusion of the merits of fuzzy logic and neural network technologies will then be made. Finally, a neuro fuzzy system for extracting objects from noisy images and its effectiveness will be described. The system consist of a multi-layer neural network with back-propagation of error and with feed back connections. In each layer there are M x N neurons (for an M x N image). Each neuron corresponds to a single pixel. Neurons in the same layer do not have any connection among themselves. Each neuron in a layer is connected to the corresponding neuron in the previous layer and to its neighbors over some window. Input is given as fuzzy set 'brightness'. The membership value for 'brightness' involves both global and local information of an image. Various measures of fuzziness of a set (like index of fuzziness, entropy, fuzzy correlation etc. ) are used to model the error in the output layer. The status of the neurons in the output layer is also considered to be a fuzzy set 'object pixels'.

Paper Details

Date Published: 28 June 1994
PDF: 25 pages
Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031208 (28 June 1994); doi: 10.1117/12.2283791
Show Author Affiliations
Sankar K. Pal, Indian Statistical Institute (India)


Published in SPIE Proceedings Vol. 10312:
Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing
Sunanda D. Mitra; Madan M. Gupta; Wolfgang F. Kraske, Editor(s)

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