{"title":"基于三层BP模型的噪声图像模式提取与识别","authors":"K. Imai, K. Gouhara, Y. Uchikawa","doi":"10.1109/IJCNN.1991.170414","DOIUrl":null,"url":null,"abstract":"The authors present a novel pattern recognition architecture using three-layered backpropagation (BP) models. The proposed architecture consists mainly of the following two completely separate functions: extraction of a target pattern and recognition of the extracted pattern. It is possible that the proposed architecture detects where and what the target pattern is. In order to realize these functions, the following networks are introduced: filtering network, position network, size network, frame-working network, and categorizing networks. Results of handwritten-letter recognition experiments show that the proposed architecture has the ability to recognize a deformed target pattern in an original image with much noise, especially lumped noises.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Pattern extraction and recognition for noisy images using the three-layered BP model\",\"authors\":\"K. Imai, K. Gouhara, Y. Uchikawa\",\"doi\":\"10.1109/IJCNN.1991.170414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors present a novel pattern recognition architecture using three-layered backpropagation (BP) models. The proposed architecture consists mainly of the following two completely separate functions: extraction of a target pattern and recognition of the extracted pattern. It is possible that the proposed architecture detects where and what the target pattern is. In order to realize these functions, the following networks are introduced: filtering network, position network, size network, frame-working network, and categorizing networks. Results of handwritten-letter recognition experiments show that the proposed architecture has the ability to recognize a deformed target pattern in an original image with much noise, especially lumped noises.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170414\",\"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] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pattern extraction and recognition for noisy images using the three-layered BP model
The authors present a novel pattern recognition architecture using three-layered backpropagation (BP) models. The proposed architecture consists mainly of the following two completely separate functions: extraction of a target pattern and recognition of the extracted pattern. It is possible that the proposed architecture detects where and what the target pattern is. In order to realize these functions, the following networks are introduced: filtering network, position network, size network, frame-working network, and categorizing networks. Results of handwritten-letter recognition experiments show that the proposed architecture has the ability to recognize a deformed target pattern in an original image with much noise, especially lumped noises.<>