{"title":"基于高斯-埃米特矩和ART神经网络的运动目标识别","authors":"Youfu Wu, Jing Wu","doi":"10.4156/JCIT.VOL5.ISSUE8.7","DOIUrl":null,"url":null,"abstract":"Moments are widely used in pattern recognition, image processing, and computer vision and multi resolution analysis. In this paper, we first printout Gaussian-Hermite moments, and propose a new method to extract the object’s features based on Gaussian-Hermite moments. Following, for training ART neural network, the moment features were inputted to ART as its parameters; so that, a classifier was realized for recognizing the moving objects. The experiment results are reported also, which show the good performance of our method.","PeriodicalId":360193,"journal":{"name":"J. Convergence Inf. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Recognizing Moving Objects Based on Gaussian-Hermite Moments and ART Neural Networks\",\"authors\":\"Youfu Wu, Jing Wu\",\"doi\":\"10.4156/JCIT.VOL5.ISSUE8.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Moments are widely used in pattern recognition, image processing, and computer vision and multi resolution analysis. In this paper, we first printout Gaussian-Hermite moments, and propose a new method to extract the object’s features based on Gaussian-Hermite moments. Following, for training ART neural network, the moment features were inputted to ART as its parameters; so that, a classifier was realized for recognizing the moving objects. The experiment results are reported also, which show the good performance of our method.\",\"PeriodicalId\":360193,\"journal\":{\"name\":\"J. Convergence Inf. Technol.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Convergence Inf. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Convergence Inf. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4156/JCIT.VOL5.ISSUE8.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognizing Moving Objects Based on Gaussian-Hermite Moments and ART Neural Networks
Moments are widely used in pattern recognition, image processing, and computer vision and multi resolution analysis. In this paper, we first printout Gaussian-Hermite moments, and propose a new method to extract the object’s features based on Gaussian-Hermite moments. Following, for training ART neural network, the moment features were inputted to ART as its parameters; so that, a classifier was realized for recognizing the moving objects. The experiment results are reported also, which show the good performance of our method.