{"title":"基于GRNN的三维相似物体识别","authors":"O. Polat, T. Yıldırım","doi":"10.1109/SIU.2006.1659863","DOIUrl":null,"url":null,"abstract":"This paper presents an approach for the recognition of similar objects automatically. In the recognition system, colour features were extracted from two dimensional (2-D) pose images of every 3-D object given and the classification of the objects was realized by using these feature vectors in general regression neural networks-GRNN. The system has been simulated with eight different objects having similar shapes and high recognition rate was obtained. The ability of recognizing many undefined objects after training with low number of samples is important property of this system","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Recognition of 3-D Similar Objects by GRNN\",\"authors\":\"O. Polat, T. Yıldırım\",\"doi\":\"10.1109/SIU.2006.1659863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach for the recognition of similar objects automatically. In the recognition system, colour features were extracted from two dimensional (2-D) pose images of every 3-D object given and the classification of the objects was realized by using these feature vectors in general regression neural networks-GRNN. The system has been simulated with eight different objects having similar shapes and high recognition rate was obtained. The ability of recognizing many undefined objects after training with low number of samples is important property of this system\",\"PeriodicalId\":415037,\"journal\":{\"name\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2006.1659863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents an approach for the recognition of similar objects automatically. In the recognition system, colour features were extracted from two dimensional (2-D) pose images of every 3-D object given and the classification of the objects was realized by using these feature vectors in general regression neural networks-GRNN. The system has been simulated with eight different objects having similar shapes and high recognition rate was obtained. The ability of recognizing many undefined objects after training with low number of samples is important property of this system
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