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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28546

Title: Image Segmentation via Fusion Neural Networks
Authors: Sih-Yin Shen;Ya-Yun Jheng;Chun-Shun Tseng;J. H. Wang
Contributors: NTOU:Department of Electrical Engineering
國立臺灣海洋大學:電機工程學系
Keywords: Neural networks;image segmentation;extended adjacency;counteracting learning;watershed Analysis
Neural networks;image segmentation;extended adjacency;counteracting learning;watershed Analysis
Date: 2006-09
Issue Date: 2011-10-21T02:38:01Z
Publisher: 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on advanced Intelligent Systems (SCIS & ISIS 2006)
Abstract: Abstract:This paper presents a self-organizing fusion neural network (SOFNN) which is effective in performing fast image segmentation. Based on a counteracting learning strategy, SOFNN employs two parameters that together control the learning rate in a counteracting manner to achieve free of over-segmentation and under-segmentation. Regions comprising an object are identified and merged in a self-organizing way, and the training process will be terminated without manual intervention. Because most training parameters are data-dependent, implementation of SOFNN is simple. Unlike existing methods that sequentially merge regions, all regions in SOFNN can be processed in parallel fashion, thus providing great potentiality for a fully parallel hardware implementation. In addition, not only the immediate neighbors are used to calculate merging criterion, but the neighboring regions surrounding the immediate regions are also referred. Such extension in adjacency helps achieve more accurate segmentation results.
Relation: pp.1135-1140
URI: http://ntour.ntou.edu.tw/handle/987654321/28546
Appears in Collections:[電機工程學系] 演講及研討會

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