基于像素线的Kinect深度图生成的三维点云聚类

Quan Qiu, W. Zheng
{"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}
引用次数: 3

摘要

提出了一种基于像素线的Kinect深度图像数据聚类算法。该算法首先对属于同一像素行的三维点进行聚类。然后对来自相邻行的单行簇进行比较匹配,实现三维簇生长。在办公室场景和温室场景下进行了实验。聚类结果表明,该算法能很好地突出小目标,但对凹凸不平的表面比较敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信