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Consensus Maximization with Linear Matrix Inequality Constraints
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017Consensus maximization has proven to be a useful tool for robust estimation. While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers. On the other hand, global methods are commonly slow because they do not exploit the structure of the problem at hand.
Pablo Speciale +5 more
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Estimation of Camera Projection Matrix Using Linear Matrix Inequalities
2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), 2016This paper proposes some methods for estimating camera projection matrix from given 3D coordinate vectors of feature points and 2D coordinate vectors of the projected feature points on the image plane. It is well-known that the problem is formulated as the L2 minimization problem of the sum of reprojection errors, which is very hard to solve because ...
Yoshimichi Ito, Yuta Oda
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Linear Matrix Inequalities in Automatic Control
2011Bernal, Miguel, Guerra, Thierry-Marie
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A Newton-like method for solving rank constrained linear matrix inequalities
Automatica, 2006John B Moore
exaly
Optimal unbiased filtering via linear matrix inequalities
Systems and Control Letters, 1998Karólos M Grigoriadis
exaly
Linear matrix inequalities in robustness analysis with multipliers
Systems and Control Letters, 1995V Balakrishnan
exaly