DocumentCode :
557760
Title :
Automatic facades segmentation using detected lines and vanishing points
Author :
Wan, Guowei ; Li, Sikun
Author_Institution :
Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
Volume :
3
fYear :
2011
fDate :
15-17 Oct. 2011
Firstpage :
1214
Lastpage :
1217
Abstract :
Segmenting facades from buildings is important to a variety of vision applications operating in outdoor urban environments which include image-based modeling, landmark recognition, autonomous navigation and urban scene understanding. Existing method based on Structure-from-Motion relies on multiple images, and the cost of computation is high, while another method based on prior knowledge about arbitrarily shaped repetitive regions cannot handle non-repetitive facades segmentation. In order to segment facades simply, fast and accurately, we propose an automatic algorithm for facade segmentation, which segments building to a set of separate facades. By first detecting line segments and corresponding vanishing points from a single image, then rectifying the image to make the vertical direction be parallel to y-direction of the image, we convert the facade-segmentation problem to a simple 1D segmentation. The experiment results show that our method is accurate, robust and efficient to facades segmentation problem.
Keywords :
computer vision; image segmentation; 1D segmentation; automatic facades segmentation; autonomous navigation; building segmentation; image-based modeling; landmark recognition; line detection; line segment detection; structure-from-motion; urban scene understanding; vanishing point detection; vision application; Buildings; Computer vision; Educational institutions; Image segmentation; Matrix decomposition; Three dimensional displays; Transmission line matrix methods; MRF; detected lines; dynamic programming; facades segmentation; image rectification; vanishing points;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
Type :
conf
DOI :
10.1109/CISP.2011.6100448
Filename :
6100448
Link To Document :
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