Yong Cheol Peter Cho, Nandhini Chandramoorthy, K. Irick, N. Vijaykrishnan
{"title":"Multiresolution Gabor Feature Extraction for Real Time Applications","authors":"Yong Cheol Peter Cho, Nandhini Chandramoorthy, K. Irick, N. Vijaykrishnan","doi":"10.1109/SiPS.2012.56","DOIUrl":null,"url":null,"abstract":"Multiresolution Gabor filters are used for feature extraction for a variety of applications. Most hardware implementations have focused on iterative mechanisms on fixed hardware for implementing the different levels of resolution. In contrast, we present a configurable architecture that enhances the resource utilization of the hardware fabric. Our results show that our implementation achieves real-time performance on 2048×1536 images and exhibits 6 times speed up over a GPU implementation. Further, our FPGA implementation achieves an energy-efficiency of processing 0.4 fps/W as compared to the GPU that achieves 0.036 fps/W.","PeriodicalId":286060,"journal":{"name":"2012 IEEE Workshop on Signal Processing Systems","volume":"320 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Workshop on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SiPS.2012.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Multiresolution Gabor filters are used for feature extraction for a variety of applications. Most hardware implementations have focused on iterative mechanisms on fixed hardware for implementing the different levels of resolution. In contrast, we present a configurable architecture that enhances the resource utilization of the hardware fabric. Our results show that our implementation achieves real-time performance on 2048×1536 images and exhibits 6 times speed up over a GPU implementation. Further, our FPGA implementation achieves an energy-efficiency of processing 0.4 fps/W as compared to the GPU that achieves 0.036 fps/W.