Fast algorithm for point-sampled model denoising

Author

Ni, Tong-guang ; Ma, Zheng-hua

Author_Institution

Dept. of Comput. Sci. & Eng., Jiangsu Polytech. Univ., Changzhou, China

Volume

7

fYear

2010

fDate

16-18 April 2010

Abstract

Denoising is an essential step in creating perfect point-sampled models. The image mean-shift filtering has been extended to 3D surface smoothing by taking the vertex normal and curvature as range component and the vertex position as the spatial component, which is not efficient. For the reason, this paper proposes to use quasi-Cauchy kernel to replace the Gauss kernel used in the Guofei. Hu´algorithm. Experiments show that our method can smooth the noise efficiently and preserve the sharp features of the surface effectively.

Keywords

Gaussian processes; computer graphics; image denoising; 3D surface smoothing; Gauss kernel; Guofei Hu algorithm; curvature; fast algorithm; image mean-shift filtering; perfect point-sampled models; point-sampled model denoising; quasiCauchy kernel; spatial component; vertex normal; vertex position; Anisotropic magnetoresistance; Computer science; Filtering algorithms; Gaussian processes; Geometry; Kernel; Noise reduction; Principal component analysis; Smoothing methods; Wiener filter; Gauss kernel; Mean-shift Procedure; Point-sampled model; quasi-Cauchy kernel;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering and Technology (ICCET), 2010 2nd International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6347-3

Type

conf

DOI

10.1109/ICCET.2010.5485909

Filename

5485909