{"title":"基于像素线的Kinect深度图生成的三维点云聚类","authors":"Quan Qiu, W. Zheng","doi":"10.1109/ICINFA.2013.6720386","DOIUrl":null,"url":null,"abstract":"A novel pixel-line based clustering algorithm for Kinect depth image data is proposed in this paper. The algorithm first clusters the three-dimensional points belonging to the same pixel row. Then the single row clusters coming from adjacent rows are compared and matched to fulfill the three-dimensional cluster growth. Experiments are carried out with both office scene and greenhouse scene. The clustering results show that the algorithm is good at highlighting small objects but is sensitive to uneven surfaces.","PeriodicalId":250844,"journal":{"name":"2013 IEEE International Conference on Information and Automation (ICIA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Pixel-line based clustering for the 3D point cloud generated by Kinect depth map\",\"authors\":\"Quan Qiu, W. Zheng\",\"doi\":\"10.1109/ICINFA.2013.6720386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel pixel-line based clustering algorithm for Kinect depth image data is proposed in this paper. The algorithm first clusters the three-dimensional points belonging to the same pixel row. Then the single row clusters coming from adjacent rows are compared and matched to fulfill the three-dimensional cluster growth. Experiments are carried out with both office scene and greenhouse scene. The clustering results show that the algorithm is good at highlighting small objects but is sensitive to uneven surfaces.\",\"PeriodicalId\":250844,\"journal\":{\"name\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Information and Automation (ICIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2013.6720386\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2013.6720386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pixel-line based clustering for the 3D point cloud generated by Kinect depth map
A novel pixel-line based clustering algorithm for Kinect depth image data is proposed in this paper. The algorithm first clusters the three-dimensional points belonging to the same pixel row. Then the single row clusters coming from adjacent rows are compared and matched to fulfill the three-dimensional cluster growth. Experiments are carried out with both office scene and greenhouse scene. The clustering results show that the algorithm is good at highlighting small objects but is sensitive to uneven surfaces.