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Representing directions for hough transforms
Citation Link: https://doi.org/10.15480/882.3628
Publikationstyp
Conference Paper
Date Issued
2006
Sprache
English
Author(s)
Institut
TORE-DOI
TORE-URI
Start Page
116
End Page
122
Citation
Proceedings of the First International Conference on Computer Vision Theory and Applications Vol. 2: 116-122 (2006)
Contribution to Conference
Publisher DOI
Publisher
SCITEPRESS
ISBN of container
972-8865-40-6
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
Parametrization
vanishing points
direction
unit sphere
Hough transform
DDC Class
004: Informatik
600: Technik
Publication version
publishedVersion
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Name
13733.pdf
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2.01 MB
Format
Adobe PDF