Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/484257
Title: An Image-Based Approach to Detection of Fake Coins
Authors: Li Liu;Yue Lu;Ching Y. Suen
Year: 2017
Publisher: IEEE
Abstract: We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.
URI: http://dl.kums.ac.ir/handle/Hannan/484257
volume: 12
issue: 5
More Information: 1227,
1239
Appears in Collections:2017

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Title: An Image-Based Approach to Detection of Fake Coins
Authors: Li Liu;Yue Lu;Ching Y. Suen
Year: 2017
Publisher: IEEE
Abstract: We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.
URI: http://dl.kums.ac.ir/handle/Hannan/484257
volume: 12
issue: 5
More Information: 1227,
1239
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7828126.pdf5.89 MBAdobe PDFThumbnail
Preview File
Title: An Image-Based Approach to Detection of Fake Coins
Authors: Li Liu;Yue Lu;Ching Y. Suen
Year: 2017
Publisher: IEEE
Abstract: We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.
URI: http://dl.kums.ac.ir/handle/Hannan/484257
volume: 12
issue: 5
More Information: 1227,
1239
Appears in Collections:2017

Files in This Item:
File Description SizeFormat 
7828126.pdf5.89 MBAdobe PDFThumbnail
Preview File