Matrix computations (3rd ed.)
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
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
International Journal of Computer Vision
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale invariant gabor descriptor-based noncooperative iris recognition
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate online kernel density estimation with Gaussian kernels
Pattern Recognition
Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
BRIEF: Computing a Local Binary Descriptor Very Fast
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Various methods were proposed to detect/match special interest points (keypoints) in images and some of them (e.g., SIFT and SURF) are among the most cited techniques in computer vision research. This paper describes an algorithm to discriminate between genuine and spurious keypoint correspondences on planar surfaces. We draw random samples of the set of correspondences, from which homographies are obtained and their principal eigenvectors extracted. Density estimation on that feature space determines the most likely true transform. Such homography feeds a cost function that gives the goodness of each keypoint correspondence. Being similar to the well-known RANSAC strategy, the key finding is that the main eigenvector of the most (genuine) homographies tends to represent a similar direction. Hence, density estimation in the eigenspace dramatically reduces the number of transforms actually evaluated to obtain reliable estimations. Our experiments were performed on hard image data sets, and pointed that the proposed approach yields effectiveness similar to the RANSAC strategy, at significantly lower computational burden, in terms of the proportion between the number of homographies generated and those that are actually evaluated.