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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40803

Title: Artificial Neural Network Approach to Authentication of Coins by Vision-based Minimization
Authors: Jang-Ping Wang;Yi-Cih Jheng;Guo-Ming Huang;Jen-Hsien Chien
Contributors: 國立臺灣海洋大學:輪機工程學系
Keywords: Authentication of coins;Minimization path;Back-propagation neural network;Eigen-sections
Date: 2009-05-07
Issue Date: 2017-02-07T01:02:42Z
Publisher: Machine Vision and Applications
Abstract: Abstract:A new inspection system, consisting of two procedures for the authentication of coins, is proposed in this paper. In the first procedure, optimum image-matching positions are found by minimizing the matching error of the test coins with their prototype coins. The second procedure is the decision-making process that inspects the coins as genuine or spurious by the Back-Propagation Neural Network combined with the concept of eigen-section. Unlike the traditional approach based on gray-level values, the quantity (8 bits) of the color’s scale has been adopted. The discrimination results are presented and discussed in this study.
Relation: 22(1), pp.87-98
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40803
Appears in Collections:[輪機工程學系] 期刊論文

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