An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
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Robust recovery of the epipolar geometry for an uncalibrated stereo rig
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Wide-baseline multiple-view correspondences
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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IEEE Transactions on Circuits and Systems for Video Technology
A particle filtering framework for joint video tracking and pose estimation
IEEE Transactions on Image Processing
Estimating correspondence between arbitrarily selected points in two widely-separated views
Advanced Engineering Informatics
Reconstruction of non-rigid 3D shapes from stereo-motion
Pattern Recognition Letters
Comparative study of algorithms for matching sets of reference points
Pattern Recognition and Image Analysis
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ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A Self-adaptive ASIFT-SH method
Advanced Engineering Informatics
Robust point pattern matching based on spectral context
Pattern Recognition
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The paper tackles the problem of feature points matching between pair of images of the same scene. This is a key problem in computer vision. The method we discuss here is a version of the SVD-matching proposed by Scott and Longuet-Higgins and later modified by Pilu, that we elaborate in order to cope with large scale variations. To this end we add to the feature detection phase a keypoint descriptor that is robust to large scale and view-point changes. Furthermore, we include this descriptor in the equations of the proximity matrix that is central to the SVD-matching. At the same time we remove from the proximity matrix all the information about the point locations in the image, that is the source of mismatches when the amount of scene variation increases. The main contribution of this work is in showing that this compact and easy algorithm can be used for severe scene variations. We present experimental evidence of the improved performance with respect to the previous versions of the algorithm.