{"title":"Multi-class fruit classification using RGB-D data for indoor robots","authors":"Lixing Jiang, A. Koch, S. Scherer, A. Zell","doi":"10.1109/ROBIO.2013.6739523","DOIUrl":null,"url":null,"abstract":"In this paper we present an effective and robust system to classify fruits under varying pose and lighting conditions tailored for an object recognition system on a mobile platform. Therefore, we present results on the effectiveness of our underlying segmentation method using RGB as well as depth cues for the specific technical setup of our robot. A combination of RGB low-level visual feature descriptors and 3D geometric properties is used to retrieve complementary object information for the classification task. The unified approach is validated using two multi-class RGB-D fruit categorization datasets. Experimental results compare different feature sets and classification methods and highlight the effectiveness of the proposed features using a Random Forest classifier.","PeriodicalId":434960,"journal":{"name":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBIO.2013.6739523","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
In this paper we present an effective and robust system to classify fruits under varying pose and lighting conditions tailored for an object recognition system on a mobile platform. Therefore, we present results on the effectiveness of our underlying segmentation method using RGB as well as depth cues for the specific technical setup of our robot. A combination of RGB low-level visual feature descriptors and 3D geometric properties is used to retrieve complementary object information for the classification task. The unified approach is validated using two multi-class RGB-D fruit categorization datasets. Experimental results compare different feature sets and classification methods and highlight the effectiveness of the proposed features using a Random Forest classifier.