Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation
Finding the largest consensus set is one of the key ideas used by the original RANSAC for removing outliers in robust-estimation. However, because of its random and non-deterministic nature, RANSAC does not fulfill the goal of consensus set maximization exactly and optimally. Based on global optimization, this paper presents a new algorithm that solves the problem exactly. We reformulate the problem as a mixed integer programming (MIP), and solve it via a tailored branch-and-bound method, where...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)|
|01_Li_Consensus_Set_Maximization_2009.pdf||643.9 kB||Adobe PDF||Request a copy|
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