{"title":"基于均值移位的快速交通图像滤波算法","authors":"Zhang Yu, Shi Zhong-ke, Wang Run-quan","doi":"10.1109/IVS.2009.5164272","DOIUrl":null,"url":null,"abstract":"This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering is provided also. Experiment results of traffic image filtering demonstrate the efficiency of the fast mean shift algorithm.","PeriodicalId":396749,"journal":{"name":"2009 IEEE Intelligent Vehicles Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fast mean shift based traffic image filtering algorithm\",\"authors\":\"Zhang Yu, Shi Zhong-ke, Wang Run-quan\",\"doi\":\"10.1109/IVS.2009.5164272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering is provided also. Experiment results of traffic image filtering demonstrate the efficiency of the fast mean shift algorithm.\",\"PeriodicalId\":396749,\"journal\":{\"name\":\"2009 IEEE Intelligent Vehicles Symposium\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Intelligent Vehicles Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2009.5164272\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Intelligent Vehicles Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2009.5164272","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast mean shift based traffic image filtering algorithm
This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering is provided also. Experiment results of traffic image filtering demonstrate the efficiency of the fast mean shift algorithm.