In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Two-View Geometry Estimation Unaffected by a Dominant Plane
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Modified pbM-Estimator Method and a Runtime Analysis Technique for the RANSAC Family
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Image-based texture replacement using multiview images
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
Adaptive Sample Consensus for Efficient Random Optimization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Mode seeking over permutations for rapid geometric model fitting
Pattern Recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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The estimation of the epipolar geometry is especially difficult where the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This work presents the Balanced Exploration and Exploitation Model Search (BEEM) algorithm that works very well especially for these difficult scenes. The BEEM algorithm handles the above two difficult cases in a unified manner. The algorithm includes the following main features: (1) Balanced use of three search techniques: global random exploration, local exploration near the current best solution and local exploitation to improve the quality of the model. (2) Exploits available prior information to accelerate the search process. (3) Uses the best found model to guide the search process, escape from degenerate models and to define an efficient stopping criterion. (4) Presents a simple and efficient method to estimate the epipolar geometry from two SIFT correspondences. (5) Uses the locality-sensitive hashing (LSH) approximate nearest neighbor algorithm for fast putative correspondences generation. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state of the art algorithms!