{"title":"基于金字塔结构的结构化神经网络的二维目标识别","authors":"V. Cantoni, A. Petrosino","doi":"10.1109/CAMP.2000.875961","DOIUrl":null,"url":null,"abstract":"In the paper we propose an approach to the realization of models inspired to biological solutions for pattern recognition. The approach is based on a hierarchical modular structure capable to learn by examples and recognize objects in digital images. The adopted techniques are based on multiresolution image correlation and neural networks. Performance on two different data sets and experimental timings on a SIMD machine are also reported.","PeriodicalId":282003,"journal":{"name":"Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"2-D object recognition by structured neural networks in a pyramidal architecture\",\"authors\":\"V. Cantoni, A. Petrosino\",\"doi\":\"10.1109/CAMP.2000.875961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the paper we propose an approach to the realization of models inspired to biological solutions for pattern recognition. The approach is based on a hierarchical modular structure capable to learn by examples and recognize objects in digital images. The adopted techniques are based on multiresolution image correlation and neural networks. Performance on two different data sets and experimental timings on a SIMD machine are also reported.\",\"PeriodicalId\":282003,\"journal\":{\"name\":\"Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMP.2000.875961\",\"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 Fifth IEEE International Workshop on Computer Architectures for Machine Perception","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMP.2000.875961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
2-D object recognition by structured neural networks in a pyramidal architecture
In the paper we propose an approach to the realization of models inspired to biological solutions for pattern recognition. The approach is based on a hierarchical modular structure capable to learn by examples and recognize objects in digital images. The adopted techniques are based on multiresolution image correlation and neural networks. Performance on two different data sets and experimental timings on a SIMD machine are also reported.
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