Representing directions for hough transforms

Publikationstyp
Conference Paper
Date Issued
2007
Sprache
English
Author(s)
Wenzel, Fabian  
Grigat, Rolf-Rainer  
Institut
Bildverarbeitungssysteme E-2  
TORE-URI
http://hdl.handle.net/11420/9957
First published in
Communications in Computer and Information Science  
Number in series
4
Start Page
330
End Page
339
Citation
Advances in Computer Graphics and Computer Vision: 330-339 (2007)
Contribution to Conference
1st International Conference on Computer Vision Theory and Applications, VISAPP 2006  
1st International Conference on Computer Graphics Theory and Applications, GRAPP 2006  
Publisher DOI
10.1007/978-3-540-75274-5_23
Scopus ID
2-s2.0-84904861719
Publisher
Springer
Many algorithms in computer vision operate with directions, i. e. with representations of 3D-points by ignoring their distance to the origin. Even though minimal parametrizations of directions may contain singularities, they can enhance convergence in optimization algorithms and are required e. g. for accumulator spaces in Hough transforms. There are numerous possibilities for parameterizing directions. However, many do not account for numerical stability when dealing with noisy data. This paper gives an overview of different parametrizations and shows their sensitivity with respect to noise. In addition to standard approaches in the field of computer vision, representations originating from the field of cartography are introduced. Experiments demonstrate their superior performance in computer vision applications in the presence of noise as they are suitable for Gaussian filtering.
Subjects
direction
Hough transform
Parametrization
unit sphere
vanishing points
DDC Class
600: Technik