Y. Onodera, Hisayoshi Watanabe, A. Taguchi, N. Iijima, M. Sone, H. Mitsui
{"title":"Translation and rotation-invariant pattern recognition method using neural network with back-propagation","authors":"Y. Onodera, Hisayoshi Watanabe, A. Taguchi, N. Iijima, M. Sone, H. Mitsui","doi":"10.1109/ICCS.1992.254891","DOIUrl":null,"url":null,"abstract":"The authors present a new translation and rotation invariant pattern recognition method using a neural network. It is clear that the left-right, up-down translation or/and rotation invariance are achieved by simple preprocessing of the original patterns without improvement of the network structure. They use a three layer feed-forward network with back-propagation for learning and recognition. The proposed method has the following merits: the net size is relative small, learning and recognition is easy. Moreover, a 100 percent recognition rate is realized by the proposed method, for the alphabet.<<ETX>>","PeriodicalId":223769,"journal":{"name":"[Proceedings] Singapore ICCS/ISITA `92","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] Singapore ICCS/ISITA `92","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCS.1992.254891","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
The authors present a new translation and rotation invariant pattern recognition method using a neural network. It is clear that the left-right, up-down translation or/and rotation invariance are achieved by simple preprocessing of the original patterns without improvement of the network structure. They use a three layer feed-forward network with back-propagation for learning and recognition. The proposed method has the following merits: the net size is relative small, learning and recognition is easy. Moreover, a 100 percent recognition rate is realized by the proposed method, for the alphabet.<>