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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28545

Title: Image Segmentation Based on Consensus Voting
Authors: Shih-Hung Chen;Ming-Jui Kuo;Jung-Hua Wang
Contributors: NTOU:Department of Electrical Engineering
Date: 2005
Issue Date: 2011-10-21T02:38:01Z
Publisher: 2005 9th IEEE International Workshop on Cellular Neural Networks and their Applications
Abstract: Abstract:This paper presents a new approach called consensus voting neural network (CVNN) which aims to perform fast image segmentation for grey images. A learning algorithm based on the principle of vote-to-consensus is developed to train CVNN. The essence of CVNN is the iterative interaction between the target neuron and its neighboring pixels, and the range of neighborhood is defined by the running mask. The neighboring neurons surrounding the target neuron collaboratively determine the label for the target neuron by "casting" their respective labels. Due to its simplicity in the updating strategy that solely employs discrete increment value in the ballot-counter, training CVNN is quite efficient.
Relation: pp.1-4
URI: http://ntour.ntou.edu.tw/handle/987654321/28545
Appears in Collections:[電機工程學系] 期刊論文

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