Xinge You, Qingjiang Hu, Duanquan Xu, Xian-cheng Fu, Qixin Sun
{"title":"Dollar bill denomination recognition algorithm based on local texture feature","authors":"Xinge You, Qingjiang Hu, Duanquan Xu, Xian-cheng Fu, Qixin Sun","doi":"10.1109/SPAC.2014.6982697","DOIUrl":null,"url":null,"abstract":"In this paper, a dollar bill denomination recognition algorithm based on local texture feature is proposed. this paper proposes a local texture feature dollar denomination recognition algorithm, this algorithm first use the between-cluster variance method about the dollar's local image binarization to enhance the effect of differences, and then through the cross algorithm to extract the local texture feature, which makes the recognition work correctly. The simulation results show that the method is fast, high precision, suitable for real-time face recognition.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAC.2014.6982697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a dollar bill denomination recognition algorithm based on local texture feature is proposed. this paper proposes a local texture feature dollar denomination recognition algorithm, this algorithm first use the between-cluster variance method about the dollar's local image binarization to enhance the effect of differences, and then through the cross algorithm to extract the local texture feature, which makes the recognition work correctly. The simulation results show that the method is fast, high precision, suitable for real-time face recognition.
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