中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 三维可视计算
Parameter optimization criteria guided 3D point cloud classification

文献类型：期刊论文

作者Li, Hongjun1; Meng, Weiliang2; Liu, Xinying2; Xiang, Shiming2; Zhang, Xiaopeng2
刊名MULTIMEDIA TOOLS AND APPLICATIONS
出版日期2019-02-01
卷号78期号:4页码:5081-5104
ISSN号1380-7501
关键词Point cloud classification Feature extraction Conditional random field Parameter optimization criterion Probabilistic neural network
DOI10.1007/s11042-018-6838-z
通讯作者Li, Hongjun(lihongjun69@bjfu.edu.cn)
英文摘要3D point cloud classification is one of the basic topics in multimedia analysis and understanding. By the construction of the discriminant model and efficient parameter optimization, point cloud classification can be achieved after the training. However, most parameter optimization methods do not guarantee the highest global classification accuracy with a high classification accuracy on smaller classes. In addition, geometric features of the point cloud are not sufficiently utilized. In this paper, we use local geometric shape features including the nearest neighbor tetrahedral volume, Gaussian curvature, the neighbourhood normal vector consistency and the neighbourhood minimum principal curvature direction consistency. We propose three discrete criteria for parameter optimization to design explicit functions, and we present concrete algorithms, in which Monte Carlo method and Probabilistic Neural Network method are employed to estimate these parameters respectively. Experimental results show that our criteria can be applied to the classification of the 3D point cloud of the scene, and can be used to improve the classification accuracy of small-scale point sets when different classes have great disparities in the number.
WOS关键词MULTISCALE
资助项目Fundamental Research Funds for the Central Universities[2015ZCQ-LY-01] ; National Natural Science Foundation of China[61372190] ; National Natural Science Foundation of China[61571439] ; National Natural Science Foundation of China[61561003] ; National Natural Science Foundation of China[61502490] ; National Natural Science Foundation of China[61501464] ; National Natural Science Foundation of China[6140001010207]
WOS研究方向Computer Science ; Engineering
语种英语
出版者SPRINGER
WOS记录号WOS:000463917200059
资助机构Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/24936]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Li, Hongjun
作者单位1.Beijing Forestry Univ, Coll Sci, Beijing, Peoples R China
2.CAS Inst Automat, LIAMA NLPR, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Hongjun,Meng, Weiliang,Liu, Xinying,et al. Parameter optimization criteria guided 3D point cloud classification[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(4):5081-5104.
APA Li, Hongjun,Meng, Weiliang,Liu, Xinying,Xiang, Shiming,&Zhang, Xiaopeng.(2019).Parameter optimization criteria guided 3D point cloud classification.MULTIMEDIA TOOLS AND APPLICATIONS,78(4),5081-5104.
MLA Li, Hongjun,et al."Parameter optimization criteria guided 3D point cloud classification".MULTIMEDIA TOOLS AND APPLICATIONS 78.4(2019):5081-5104.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。