28 June 1994 Neuro-fuzzy image processing: relevance and feasibility
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Proceedings Volume 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing; 1031208 (1994) https://doi.org/10.1117/12.2283791
Event: SPIE Institutes for Advanced Optical Technologies 12, 1994, Bellingham, WA, United States
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'.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sankar K. Pal, Sankar K. Pal, } "Neuro-fuzzy image processing: relevance and feasibility", Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031208 (28 June 1994); doi: 10.1117/12.2283791; https://doi.org/10.1117/12.2283791
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