MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
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Rigid motion estimation from image point correspondences is an overconstrained problem that can be solved by minimizing an adequate cost function. Given the unreliable nature of image point correspondences, they must be divided into two categories: inliers and outliers. Finding the correct camera motion and discarding the outliers is a coupled problem usually solved by a random search of the solution space. This article proposes adaptive and hybrid genetic approaches to improve the efficiency of this search. We build on top of the GASAC algorithm that has been recently presented for solving problems in geometric computer vision. GASAC is modified to address the specific issues of camera motion estimation such as outlier ratios above 50% due to wide-baseline image acquisition and an adequate choice of a fitness function. In order to avoid local minima, we propose three adaptive strategies: varying the mutation probability, resampling the lowest ranked individuals, and using a hybrid approach that combines GASAC with simulated annealing. Results are validated on publicly available benchmark images, and it is shown that the proposed genetic approaches outperform the standard RANSAC search used among computer vision practitioners.