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1 April 1998 Encoder-segmented neural network (ESNN) for image segmentation
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Proceedings Volume 3307, Applications of Artificial Neural Networks in Image Processing III; (1998) https://doi.org/10.1117/12.304649
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ning Li, Paul S. Y. Wu, and Yafei F. Guo "Encoder-segmented neural network (ESNN) for image segmentation", Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); https://doi.org/10.1117/12.304649
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