Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction

Qifa Ke and Takeo Kanade
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, July, 2006, pp. 1199 - 1205.


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Abstract
Geometric reconstruction problems in computer vision can be solved by minimizing the maximum of reprojection errors, i.e., the Linfty-norm. Unlike L2-norm (sum of squared reprojection errors), the global minimum of Linfty-norm can be efficiently achieved by quasiconvex optimization. However, the maximum of reprojection errors is the meaningful measure to minimize only when the measurement noises are independent and identically distributed at every 2D feature point and in both directions in the image. This is rarely the case in real data, where the positional noise not only varies at different features, but also has strong directionality. In this paper, we incorporate the directional uncertainty model into a quasiconvex optimization framework, in which global minimum of meaningful errors can be efficiently achieved, and accurate geometric reconstructions can be obtained from feature points that contain high directional uncertainty.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 7

Text Reference
Qifa Ke and Takeo Kanade, "Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction," 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, July, 2006, pp. 1199 - 1205.

BibTeX Reference
@inproceedings{Ke_2006_5658,
   author = "Qifa Ke and Takeo Kanade",
   title = "Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction",
   booktitle = "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
   pages = "1199 - 1205",
   month = "July",
   year = "2006",
   volume = "1",
}