Robust regression and outlier detection
Robust regression and outlier detection
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
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
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Invariant-Based Classification of Inliers and Outliers for Image Matching
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
An Evaluation of Affine Invariant-Based Classification for Image Matching
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Using Local Affine Invariants to Improve Image Matching
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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This paper presents a new model fitting approach to classify tentative feature matches as inliers or outliers during wide baseline image matching. The results show this approach increases the efficiency over traditional approaches (e.g. RANSAC) and other recently published approaches. During wide baseline image matching a feature matching algorithm generates a set of tentative matches. Our approach then classifies matches as inliers or outliers by determining if the matches are consistent with an affine model. In image pairs related by an affine transformation the ratios of areas of corresponding shapes is invariant. Our approach uses this invariant by sampling matches in a local region. Triangles are then formed from the matches and the ratios of areas of corresponding triangles are computed. If the resulting ratios of areas are consistent, then the sampled matches are classified as inliers. The resulting reduced inlier set is then processed through a model fitting step to generate the final set of inliers. In this paper we present experimental results comparing our approach to traditional model fitting and other affine based approaches. The results show the new method maintains the accuracy of other approaches while significantly increasing the efficiency of wide baseline matching for planar scenes.