DocumentCode
2111890
Title
The Three-Dimensional Imaging Based on Mean Shift Algorithm
Author
Wang, Limei ; Wang, Jianwen ; Liu, Bin ; Xu, Qian
Author_Institution
Coll. of Electr. & Inf. Eng., Shaanxi Univ. of Sci. & Technol., Xi´´an, China
Volume
1
fYear
2010
fDate
7-8 Aug. 2010
Firstpage
506
Lastpage
509
Abstract
The paper presents the surface clustering algorithm to remove the noise points of point cloud data for the three-dimensional imaging. The mean shift algorithm makes each sampling point to shift to the local maximum value of the kernel density function, and removes the noise points of point cloud data. Experiments show that the algorithm makes full use of the correlation and the local information of sampling points, not only removes most of the noise points but also retains the details, and gets a better three-dimensional effect.
Keywords
image denoising; pattern clustering; kernel density function; local maximum value; mean shift algorithm; point cloud data; surface clustering algorithm; three dimensional imaging; Clouds; Clustering algorithms; Computer graphics; Imaging; Kernel; Noise; Smoothing methods; likelihood function; mean shift; three-dimensional imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Management Engineering (ISME), 2010 International Conference of
Conference_Location
Xi´an
Print_ISBN
978-1-4244-7669-5
Electronic_ISBN
978-1-4244-7670-1
Type
conf
DOI
10.1109/ISME.2010.138
Filename
5573599
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