Range image segmentation and fitting by residual consensus

Author

Yu, Xinming ; Bui, T.D. ; Krzyzak, A.

Author_Institution

Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada

fYear

1992

fDate

15-18 Jun 1992

Firstpage

657

Lastpage

660

Abstract

The authors randomly sample appropriate range image points and solve equations determined by these points for the parameters of selected primitive type. From K samples they measure residual consensus to choose one set of sample points that determines an equation having the best fit for the largest homogeneous surface patch in the current processing region. The residual consensus is measured by a compressed histogram method that works at various noise levels. The estimated surface patch is extracted out of the processing region to avoid further computation. A genetic algorithm is used to accelerate the search speed

Keywords

genetic algorithms; image segmentation; best fit; compressed histogram; genetic algorithm; homogeneous surface patch; noise levels; processing region; range image fitting; range image points; range image segmentation; residual consensus; search speed; Computer science; Electric breakdown; Equations; Image segmentation; Least squares approximation; Least squares methods; Robustness; Sampling methods; Sorting; Surface fitting;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on

Conference_Location

Champaign, IL

ISSN

1063-6919

Print_ISBN

0-8186-2855-3

Type

conf

DOI

10.1109/CVPR.1992.223208

Filename

223208