{"title":"弹性图匹配的数字大规模集成电路结构及其FPGA实现","authors":"T. Nakano, T. Morie","doi":"10.1109/IJCNN.2005.1555935","DOIUrl":null,"url":null,"abstract":"The elastic graph matching (EGM) is known as an excellent algorithm in applications of human face recognition. This paper proposes a digital LSI architecture for EGM and a face/object recognition system using its FPGA implementation. In the EGM, the matching evaluation point graph is distorted to find the best trade-off between better matching in the feature space and less distortion of the evaluation point graph. In the proposed architecture, cache memory stores calculation results at the evaluation points and those at their neighboring pixels to reduce the calculation amount. In the FPGA implementation with a system clock of 48 MHz, EGM between the input and one memorized image can be performed in about 1 ms.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A digital LSI architecture of elastic graph matching and its FPGA implementation\",\"authors\":\"T. Nakano, T. Morie\",\"doi\":\"10.1109/IJCNN.2005.1555935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The elastic graph matching (EGM) is known as an excellent algorithm in applications of human face recognition. This paper proposes a digital LSI architecture for EGM and a face/object recognition system using its FPGA implementation. In the EGM, the matching evaluation point graph is distorted to find the best trade-off between better matching in the feature space and less distortion of the evaluation point graph. In the proposed architecture, cache memory stores calculation results at the evaluation points and those at their neighboring pixels to reduce the calculation amount. In the FPGA implementation with a system clock of 48 MHz, EGM between the input and one memorized image can be performed in about 1 ms.\",\"PeriodicalId\":365690,\"journal\":{\"name\":\"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2005.1555935\",\"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. 2005 IEEE International Joint Conference on Neural Networks, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2005.1555935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A digital LSI architecture of elastic graph matching and its FPGA implementation
The elastic graph matching (EGM) is known as an excellent algorithm in applications of human face recognition. This paper proposes a digital LSI architecture for EGM and a face/object recognition system using its FPGA implementation. In the EGM, the matching evaluation point graph is distorted to find the best trade-off between better matching in the feature space and less distortion of the evaluation point graph. In the proposed architecture, cache memory stores calculation results at the evaluation points and those at their neighboring pixels to reduce the calculation amount. In the FPGA implementation with a system clock of 48 MHz, EGM between the input and one memorized image can be performed in about 1 ms.