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A convex method to minimal problems for fundamental matrix estimation with radial distortion
Author(s): Zuoluo Zhang; Zongqing Lu; Qingmin Liao
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Paper Abstract

This paper focuses on the problem of estimating the fundamental matrix with unknown radial distortion. The general method to the problem is Gröbner basis method. That solves nontrivial polynomial equations formed by a pair of correspondences under one-parameter division model for radial distortion, which is nonconvex and no noise-resistant. Using results from polynomial optimization tools and rank minimization method, this paper shows that the problem can be solved as a sequence of convex semi-definite programs. In the experiments, we show that the proposed method works well and is more noise-resistant.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061C (9 August 2018); doi: 10.1117/12.2503142
Show Author Affiliations
Zuoluo Zhang, Tsinghua Univ. (China)
Zongqing Lu, Tsinghua Univ. (China)
Qingmin Liao, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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