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
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized RANSAC with Sequential Probability Ratio Test
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond RANSAC: User Independent Robust Regression
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust Scale Estimation from Ensemble Inlier Sets for Random Sample Consensus Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Hill climbing algorithm for random sample consensus methods
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Balanced exploration and exploitation model search for efficient epipolar geometry estimation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Mode seeking over permutations for rapid geometric model fitting
Pattern Recognition
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This paper approaches random optimization problem with adaptive sampling, which exploits knowledge about data structure obtained from historical samples. The proposal distribution is adaptive so that it invests more searching efforts on high likelihood regions. In this way, the probability of reaching the global optimum is improved. The method demonstrates improved performance as compared with standard RANSAC and related adaptive methods, for line/plane/ellipse fitting and pose estimation problems.