R. Bolle, J. Connell, N. Haas, R. Mohan, G. Taubin
{"title":"VeggieVision: a produce recognition system","authors":"R. Bolle, J. Connell, N. Haas, R. Mohan, G. Taubin","doi":"10.1109/ACV.1996.572062","DOIUrl":null,"url":null,"abstract":"The authors present an automatic product 1D system (\"VeggieVision\"), intended to ease the produce checkout process. The system consists of an integrated scale and imaging system with a user-friendly interface. When a produce item is placed on the scale, an image is taken. A variety of features, color, texture (shape, density), are then extracted. These features are compared to stored \"signatures\" which were obtained by prior system training (either on-line or off-line). Depending on the certainty of the classification, the final decision is made either by the system or by a human from a number of choices selected by the system. Over 95% of the time, the correct produce classification is in the top four choices.","PeriodicalId":222106,"journal":{"name":"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"101","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACV.1996.572062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 101
Abstract
The authors present an automatic product 1D system ("VeggieVision"), intended to ease the produce checkout process. The system consists of an integrated scale and imaging system with a user-friendly interface. When a produce item is placed on the scale, an image is taken. A variety of features, color, texture (shape, density), are then extracted. These features are compared to stored "signatures" which were obtained by prior system training (either on-line or off-line). Depending on the certainty of the classification, the final decision is made either by the system or by a human from a number of choices selected by the system. Over 95% of the time, the correct produce classification is in the top four choices.