{"title":"使用GPU进行高效的边缘检测","authors":"Kohei Ogawa, Yasuaki Ito, K. Nakano","doi":"10.1109/IC-NC.2010.13","DOIUrl":null,"url":null,"abstract":"Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). The main contribution of this paper is to implement a Canny edge detection algorithm on CUDA. The experimental result shows that our implementation of Canny edge detection algorithm on CUDA achieves a speedup factor of 61 over a conventional software implementation.","PeriodicalId":375145,"journal":{"name":"2010 First International Conference on Networking and Computing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"120","resultStr":"{\"title\":\"Efficient Canny Edge Detection Using a GPU\",\"authors\":\"Kohei Ogawa, Yasuaki Ito, K. Nakano\",\"doi\":\"10.1109/IC-NC.2010.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). The main contribution of this paper is to implement a Canny edge detection algorithm on CUDA. The experimental result shows that our implementation of Canny edge detection algorithm on CUDA achieves a speedup factor of 61 over a conventional software implementation.\",\"PeriodicalId\":375145,\"journal\":{\"name\":\"2010 First International Conference on Networking and Computing\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"120\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 First International Conference on Networking and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC-NC.2010.13\",\"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 First International Conference on Networking and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC-NC.2010.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture). The main contribution of this paper is to implement a Canny edge detection algorithm on CUDA. The experimental result shows that our implementation of Canny edge detection algorithm on CUDA achieves a speedup factor of 61 over a conventional software implementation.
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