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Proceedings Paper

Encoder-segmented neural network (ESNN) for image segmentation
Author(s): Ning Li; Paul S. Y. Wu; Yafei F. Guo
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Paper Abstract

Neural networks have been applied to many kinds of image processing with well performance. When dealing with the large image, a large number of neurons is required so as to (1) make the construction model more complex, (2) make the speed of processing slower than the traditional methods due to heavy computation load. In this paper, an encoder- segmented neural network is constructed for image segmentation in which the available data can be obtained by a weight matrix containing maximum region information when a large number of input data are compressed by encoder network, meantime, the fuzzy clustering strategy applied on Hopfield neural network for the fine segmentation eliminates the tedious work of finding weighting factors. The experimental results indicate the performance of image segmentation can be improved effectively.

Paper Details

Date Published: 1 April 1998
PDF: 8 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304649
Show Author Affiliations
Ning Li, City Univ. of Hong Kong (China)
Paul S. Y. Wu, City Univ. of Hong Kong (Hong Kong)
Yafei F. Guo, City Univ. of Hong Kong (United States)


Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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