{"title":"研究了一种局部离散小波变换递推算法的性能","authors":"V. N. Kopenkov, V. Myasnikov","doi":"10.1109/ICPR.2010.1081","DOIUrl":null,"url":null,"abstract":"We experimentally compare the performance of two fast algorithms for computing the local discrete wavelet transform of one-dimensional signals: the Mallatalgorithm and a recursive algorithm. For the comparison purposes, we analyze Haar wavelet bases for one and two-dimensional signals, an extension of the Haar basis with the scale coefficient 3, and biorthogonal polynomial spline wavelets with finite support.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research the Performance of a Recursive Algorithm of the Local Discrete Wavelet Transform\",\"authors\":\"V. N. Kopenkov, V. Myasnikov\",\"doi\":\"10.1109/ICPR.2010.1081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We experimentally compare the performance of two fast algorithms for computing the local discrete wavelet transform of one-dimensional signals: the Mallatalgorithm and a recursive algorithm. For the comparison purposes, we analyze Haar wavelet bases for one and two-dimensional signals, an extension of the Haar basis with the scale coefficient 3, and biorthogonal polynomial spline wavelets with finite support.\",\"PeriodicalId\":309591,\"journal\":{\"name\":\"2010 20th International Conference on Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 20th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2010.1081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 20th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2010.1081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research the Performance of a Recursive Algorithm of the Local Discrete Wavelet Transform
We experimentally compare the performance of two fast algorithms for computing the local discrete wavelet transform of one-dimensional signals: the Mallatalgorithm and a recursive algorithm. For the comparison purposes, we analyze Haar wavelet bases for one and two-dimensional signals, an extension of the Haar basis with the scale coefficient 3, and biorthogonal polynomial spline wavelets with finite support.
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