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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28505

Title: Improved Segmentation Based on Probabilistic Labeling
Authors: Kai-Chieh Yang;Ming-Chi Jhuang;Chun-Shun Tseng;Jhan-Syuan Yu;Jung-Hua Wang
Contributors: NTOU:Department of Electrical Engineering
Keywords: image segmentation;graph theory;Dirichlet problem;harmonic function;watershed analysis
Date: 2008
Issue Date: 2011-10-21T02:37:49Z
Publisher: International Conference on Bioinformatics & Computational Biology
Abstract: Abstract:This paper presents an improved multi-object segmentation algorithm based on probabilistic labeling. First, a critical look is focused on utilizing vector calculus operator and combinational operator to rewrite Dirichlet integral into a matrix form, and boundary condition is defined to obtain the needed harmonic function. The only unique parameterβthat dominantly affects the segmentation performance is characterized. According to the result, we propose an improved parameter that changes the value ofβon the basis of pixel-by-pixel, rather than the use of a fixed constantβthroughout the whole image. Furthermore, a pre-process involving the use of watershed analysis is applied to smooth the effect of high frequency components in the input image, so that better noise tolerance and more accurate object contours can be obtained.
Relation: BIOCOMP 2008, pp.563-567
URI: http://ntour.ntou.edu.tw/handle/987654321/28505
Appears in Collections:[電機工程學系] 演講及研討會

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