{"title":"用于实时目标检测的积分图像流处理","authors":"C. Messom, A. Barczak","doi":"10.1109/PDCAT.2008.46","DOIUrl":null,"url":null,"abstract":"This paper presents the design and evaluation of the stream processing implementation of the integral image algorithm. The integral image is a key component of many image processing algorithms in particular the Haar-like feature based systems. Modern GPUs provide a large number of processors with a peak floating point performance that is significantly higher than current general CPUs. This results in significant performance improvement when the Integral Image calculation for large input images is offloaded onto the GPU of the system.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Stream Processing of Integral Images for Real-Time Object Detection\",\"authors\":\"C. Messom, A. Barczak\",\"doi\":\"10.1109/PDCAT.2008.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the design and evaluation of the stream processing implementation of the integral image algorithm. The integral image is a key component of many image processing algorithms in particular the Haar-like feature based systems. Modern GPUs provide a large number of processors with a peak floating point performance that is significantly higher than current general CPUs. This results in significant performance improvement when the Integral Image calculation for large input images is offloaded onto the GPU of the system.\",\"PeriodicalId\":282779,\"journal\":{\"name\":\"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDCAT.2008.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDCAT.2008.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stream Processing of Integral Images for Real-Time Object Detection
This paper presents the design and evaluation of the stream processing implementation of the integral image algorithm. The integral image is a key component of many image processing algorithms in particular the Haar-like feature based systems. Modern GPUs provide a large number of processors with a peak floating point performance that is significantly higher than current general CPUs. This results in significant performance improvement when the Integral Image calculation for large input images is offloaded onto the GPU of the system.