{"title":"基于SIMD超立方体的平行尺度空间构建","authors":"A. C. Panda, H. Mehrotra, B. Majhi","doi":"10.1109/WICT.2012.6409151","DOIUrl":null,"url":null,"abstract":"This paper proposes parallel scale space construction of Scale Invariant Feature Transform (SIFT) using SIMD hypercube. The parallel SIFT approach is used for iris feature extraction. The input iris images and Gaussian filters are mapped to each processor in the hypercube and convolution takes place in each processor concurrently. The time complexity of parallel algorithm is O(N2) whereas sequential algorithm performs with complexity of O(lsN2), where l is the number of octaves, s is the number of Gaussian scale levels within an octave for N2 sized iris image.","PeriodicalId":445333,"journal":{"name":"2012 World Congress on Information and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel scale space construction using SIMD hypercube\",\"authors\":\"A. C. Panda, H. Mehrotra, B. Majhi\",\"doi\":\"10.1109/WICT.2012.6409151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes parallel scale space construction of Scale Invariant Feature Transform (SIFT) using SIMD hypercube. The parallel SIFT approach is used for iris feature extraction. The input iris images and Gaussian filters are mapped to each processor in the hypercube and convolution takes place in each processor concurrently. The time complexity of parallel algorithm is O(N2) whereas sequential algorithm performs with complexity of O(lsN2), where l is the number of octaves, s is the number of Gaussian scale levels within an octave for N2 sized iris image.\",\"PeriodicalId\":445333,\"journal\":{\"name\":\"2012 World Congress on Information and Communication Technologies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 World Congress on Information and Communication Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WICT.2012.6409151\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 World Congress on Information and Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WICT.2012.6409151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel scale space construction using SIMD hypercube
This paper proposes parallel scale space construction of Scale Invariant Feature Transform (SIFT) using SIMD hypercube. The parallel SIFT approach is used for iris feature extraction. The input iris images and Gaussian filters are mapped to each processor in the hypercube and convolution takes place in each processor concurrently. The time complexity of parallel algorithm is O(N2) whereas sequential algorithm performs with complexity of O(lsN2), where l is the number of octaves, s is the number of Gaussian scale levels within an octave for N2 sized iris image.
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