DocumentCode
2430944
Title
Urban building detection by visual and geometrical features
Author
Trinh, Hoang-Hon ; Kim, Dae-Nyeon ; Jo, Kang-Huyn
Author_Institution
Univ. of Ulsan, Ulsan
fYear
2007
fDate
17-20 Oct. 2007
Firstpage
1779
Lastpage
1784
Abstract
This paper describes an approach to detect the buildings in the urban environment. Visual and geometrical features of line segments are used to classify the building in the images. The buildings are also distinguished with other objects like sky, tree, bush and roads. Firstly, the line segments of building and non-building patterns are separated. The natural features are the contrast between two neighbored regions of segment, vanishing points, the appeared density, the vertical and horizontal alongside distributions. Those features are used to step-by-step reduce the segments of non-building pattern. The rests called the basic segments are grouped to create a mesh of skewed parallelograms. Each mesh represents a partial face of buildings. Finally, the faces or facets of building are detected by combining the neighbored partial faces. The building facet is refined again by its area. The proposed approach has been experimented for over 800 test images with the high rate of detection results.
Keywords
building; image classification; image segmentation; mesh generation; building classification; building facet; geometrical features; image classification; line segments; nonbuilding pattern; skewed parallelogram mesh; urban building detection; visual features; Automatic control; Buildings; Face detection; Image databases; Image segmentation; Multilevel systems; Object detection; Personal communication networks; Roads; Testing; Building and non-building patterns; skewed parallelogram; vanishing points;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation and Systems, 2007. ICCAS '07. International Conference on
Conference_Location
Seoul
Print_ISBN
978-89-950038-6-2
Electronic_ISBN
978-89-950038-6-2
Type
conf
DOI
10.1109/ICCAS.2007.4406633
Filename
4406633
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