{"title":"一个自动设计CNN-UM节目的工具","authors":"G. E. Pazienza, X. Vilasís-Cardona, K. Karacs","doi":"10.1109/ECCTD.2007.4529640","DOIUrl":null,"url":null,"abstract":"Programs for the Cellular Neural Network - Universal Machine are usually designed explicitly, and there is no method to create them automatically. In this paper we present a tool, based on a genetic approach, capable of determining what CNN templates compose a CNN-UM program performing a given image processing task, and in which order they must be applied to the input. The effectiveness of our system is demonstrated experimentally on real-life problems, like the route number detection on public transport vehicles.","PeriodicalId":445822,"journal":{"name":"2007 18th European Conference on Circuit Theory and Design","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An automatic tool to design CNN-UM programs\",\"authors\":\"G. E. Pazienza, X. Vilasís-Cardona, K. Karacs\",\"doi\":\"10.1109/ECCTD.2007.4529640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Programs for the Cellular Neural Network - Universal Machine are usually designed explicitly, and there is no method to create them automatically. In this paper we present a tool, based on a genetic approach, capable of determining what CNN templates compose a CNN-UM program performing a given image processing task, and in which order they must be applied to the input. The effectiveness of our system is demonstrated experimentally on real-life problems, like the route number detection on public transport vehicles.\",\"PeriodicalId\":445822,\"journal\":{\"name\":\"2007 18th European Conference on Circuit Theory and Design\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 18th European Conference on Circuit Theory and Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECCTD.2007.4529640\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 18th European Conference on Circuit Theory and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECCTD.2007.4529640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Programs for the Cellular Neural Network - Universal Machine are usually designed explicitly, and there is no method to create them automatically. In this paper we present a tool, based on a genetic approach, capable of determining what CNN templates compose a CNN-UM program performing a given image processing task, and in which order they must be applied to the input. The effectiveness of our system is demonstrated experimentally on real-life problems, like the route number detection on public transport vehicles.
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