In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimization Criteria Used in Two-View Motion Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex Optimization
Quasiconvex Optimization for Robust Geometric Reconstruction
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple View Geometry and the L_"-norm
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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Computing the fundamental matrix is the first step of many computer vision applications including camera calibration, image rectification and structure from motion. A new method for the estimation of the fundamental matrix from point correspondences is presented. The minimization of the geometric error is performed based L- infinity norm minimization framework. A single global minimum exists and it may be found by SOCP (Second-Order Cone Programming), which is a standard technique in convex optimization. In a SOCP a linear function is minimized over the intersection of an affine set and the product of second-order (quadratic) cones. Several efficient primal-dual interior-point methods for SOCP have been developed. Experiments on real images show that this method provides a more accurate estimate of the fundamental matrix and superior to previous approaches, and the method is no need for normalization of the image coordinates.