{"title":"SIMD超立方算法的完全欧氏距离变换","authors":"Henry Y. H. Chuang, Ling Chen","doi":"10.1109/ICAPP.1995.472282","DOIUrl":null,"url":null,"abstract":"The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its Euclidean distance to the nearest foreground pixel. A parallel EDT algorithm on SIMD hypercube computer is presented here. For an n/spl times/n image, the algorithm has a time complexity of O(n) on an n/sup 2/ nodes machine. With modifications to minimize dependency among partitions, the algorithm can be adapted to compute large EDT problems on smaller hypercubes. On a hypercube of t/sup 2/ nodes, the time complexity of the modified algorithm is O(n/sup 2//t log n/t).<<ETX>>","PeriodicalId":448130,"journal":{"name":"Proceedings 1st International Conference on Algorithms and Architectures for Parallel Processing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"SIMD hypercube algorithm for complete Euclidean distance transform\",\"authors\":\"Henry Y. H. Chuang, Ling Chen\",\"doi\":\"10.1109/ICAPP.1995.472282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its Euclidean distance to the nearest foreground pixel. A parallel EDT algorithm on SIMD hypercube computer is presented here. For an n/spl times/n image, the algorithm has a time complexity of O(n) on an n/sup 2/ nodes machine. With modifications to minimize dependency among partitions, the algorithm can be adapted to compute large EDT problems on smaller hypercubes. On a hypercube of t/sup 2/ nodes, the time complexity of the modified algorithm is O(n/sup 2//t log n/t).<<ETX>>\",\"PeriodicalId\":448130,\"journal\":{\"name\":\"Proceedings 1st International Conference on Algorithms and Architectures for Parallel Processing\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1st International Conference on Algorithms and Architectures for Parallel Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAPP.1995.472282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1st International Conference on Algorithms and Architectures for Parallel Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAPP.1995.472282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SIMD hypercube algorithm for complete Euclidean distance transform
The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its Euclidean distance to the nearest foreground pixel. A parallel EDT algorithm on SIMD hypercube computer is presented here. For an n/spl times/n image, the algorithm has a time complexity of O(n) on an n/sup 2/ nodes machine. With modifications to minimize dependency among partitions, the algorithm can be adapted to compute large EDT problems on smaller hypercubes. On a hypercube of t/sup 2/ nodes, the time complexity of the modified algorithm is O(n/sup 2//t log n/t).<>
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
