DocumentCode
2502515
Title
3D Model Comparison through Kernel Density Matching
Author
Wang, Yiming ; Lu, Tong ; Gao, Rongjun ; Liu, Wenyin
Author_Institution
State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
fYear
2010
fDate
23-26 Aug. 2010
Firstpage
3159
Lastpage
3162
Abstract
A novel 3D shape matching method is proposed in this paper. We first extract angular and distance feature pairs from pre-processed 3D models, then estimate their kernel densities after quantifying the feature pairs into a fixed number of bins. During 3D matching, we adopt the KL-divergence as a distance of 3D comparison. Experimental results show that our method is effective to match similar 3D shapes, and robust to model deformations or rotation transformations.
Keywords
feature extraction; image matching; shape recognition; solid modelling; 3D model comparison; 3D shape matching; KL-divergence; angular feature pairs extraction; distance feature pairs extraction; kernel density matching; Computational modeling; Estimation; Feature extraction; Kernel; Shape; Solid modeling; Three dimensional displays; 3D shape maching; kernel density estimate;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location
Istanbul
ISSN
1051-4651
Print_ISBN
978-1-4244-7542-1
Type
conf
DOI
10.1109/ICPR.2010.773
Filename
5597174
Link To Document