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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/38163

Title: Entropy-weighted Bayesian approach to edge finding for object perception
Authors: Chun-Shun Tseng
Chiao-Wei Lin
Chang-De Lin
Shan-Chun Tsai
Jung-Hua Wang
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: visual perception
Bayes methods
edge detection
feature extraction
image texture
median filters
Date: 2011
Issue Date: 2016-08-10T01:40:51Z
Publisher: System Integration (SII)
Abstract: Abstract: Why edge feature is considered far most important for establishing a perceptual contour in human vision system is based on two dependent viewpoints (a) robust ability to define/extract edges from heterogeneous objects or textures and (b) a subsequent step to decide which edges are significant enough to be preserved for object perception, namely the perceptual edges. In this paper, we present a method not only capable of finding perceptual edges but also allowing them to be used for constructing contours with good continuity. The method mainly comprises two stages: (i) a linear mask filter and non-linear filters (median filter and morphology) are applied to obtain fine-and coarse-edge features, respectively. (ii) An algorithm based on Entropy-weighted Bayesian decision making used to determine perceptual edges is carried out. Extensive simulation results are provided to show noise resistance, and the capability of approximating human visual perception is revealed by testing results of gestalt images.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/38163
Appears in Collections:[電機工程學系] 博碩士論文

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