DocumentCode
177009
Title
Optimal line feature generation from low-level line segments under RANSAC framework
Author
Li Haifeng ; Chen Rong
Author_Institution
Coll. of Comput. Sci. & Technol., Civil Aviation Univ. of China, Tianjin, China
fYear
2014
fDate
May 31 2014-June 2 2014
Firstpage
4589
Lastpage
4593
Abstract
The low-level line segment features have low accuracy as they are more easily affected by the noise and differeent line segment detectors. Furthermore, the line segment is not a good feature for matching across multiple views when we need to finish the 3D reconstruction. However, the line feature is more robust for the noise. In this paper, a kind of line feature, ideal line, is defined and optimally estimated by clustering the line segments under RANSAC framework. The physical experiments are carried out to verify the proposed estimation method.
Keywords
image reconstruction; image segmentation; maximum likelihood estimation; 3D reconstruction; RANSAC framework; ideal line; line segment detectors; low-level line segment features; optimal line feature generation; Educational institutions; Feature extraction; Image segmentation; Maximum likelihood estimation; Merging; Noise; Ideal Line; Line Segment; Maximum Likelihood Estimation; RANSAC;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location
Changsha
Print_ISBN
978-1-4799-3707-3
Type
conf
DOI
10.1109/CCDC.2014.6852992
Filename
6852992
Link To Document