Translator Disclaimer
9 August 2018 A convex method to minimal problems for fundamental matrix estimation with radial distortion
Author Affiliations +
Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061C (2018) https://doi.org/10.1117/12.2503142
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zuoluo Zhang, Zongqing Lu, and Qingmin Liao "A convex method to minimal problems for fundamental matrix estimation with radial distortion", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061C (9 August 2018); https://doi.org/10.1117/12.2503142
PROCEEDINGS
8 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT


Back to Top