{"title":"A Fast Alternative for Template Matching: An ObjectCode Method","authors":"Yiping Shen, Shuxiao Li, Chenxu Wang, Hongxing Chang","doi":"10.1109/ACPR.2013.80","DOIUrl":null,"url":null,"abstract":"In this paper an ObjectCode method is presented for fast template matching. Firstly, Local Binary Patterns are adopted to get the patterns for the template and the search image, respectively. Then, a selection strategy is proposed to choose a small portion of pixels (on average 1.87%) from the template, whose patterns are concatenated to form an ObjectCode representing the characteristics of the interested target region. For the candidates in the search image, we get the candidate codes using the selected pixels from the template accordingly. Finally, the similarities between the ObjectCode and the candidate codes are calculated efficiently by a new distance measure based on Hamming distance. Extensive experiments demonstrated that our method is 13.7 times faster than FFT-based template matching and 1.1 times faster than Two-stage Partial Correlation Elimination (TPCE) with similar performances, thus is a fast alternative for current template matching methods.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"85 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 2nd IAPR Asian Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2013.80","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper an ObjectCode method is presented for fast template matching. Firstly, Local Binary Patterns are adopted to get the patterns for the template and the search image, respectively. Then, a selection strategy is proposed to choose a small portion of pixels (on average 1.87%) from the template, whose patterns are concatenated to form an ObjectCode representing the characteristics of the interested target region. For the candidates in the search image, we get the candidate codes using the selected pixels from the template accordingly. Finally, the similarities between the ObjectCode and the candidate codes are calculated efficiently by a new distance measure based on Hamming distance. Extensive experiments demonstrated that our method is 13.7 times faster than FFT-based template matching and 1.1 times faster than Two-stage Partial Correlation Elimination (TPCE) with similar performances, thus is a fast alternative for current template matching methods.