English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26988/38789
Visitors : 2354504      Online Users : 183
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28602

Title: Self-organizing Fusion Neural Networks
Authors: Jung-Hua Wang;Chun-Shun Tseng;Sih-Yin Shen;Ya-Yun Jheng
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;clustering;counteracting learning;watershed
Date: 2007
Issue Date: 2011-10-21T02:38:25Z
Publisher: Journal of Advanced Computational Intelligence and Intelligent Informatics
Abstract: Abstract:This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regional and inter-regional local statistics is used. Such extension in adjacency not only helps achieve more accurate segmentation results, but also improves input noise tolerance. Through iterating the three phases of simultaneous updating, self-organizing fusion, and extended merging, the training process converges without manual intervention, thereby conveniently obviating the need of pre-specifying the terminating number of objects. 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.
Relation: 11(6), PP.610-619
URI: http://ntour.ntou.edu.tw/handle/987654321/28602
Appears in Collections:[電機工程學系] 期刊論文

Files in This Item:

File Description SizeFormat
index.html0KbHTML274View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback