Large baseline matching of scale invariant features

Authors:
Elisabetta Delponte;Francesco Isgrò;Francesca Odone;Alessandro Verri
Affiliations:
INFM – DISI, Università di Genova, Genova, Italy;INFM – DISI, Università di Genova, Genova, Italy;INFM – DISI, Università di Genova, Genova, Italy;INFM – DISI, Università di Genova, Genova, Italy
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

Citing 10
Cited 0

Robust regression methods for computer vision: a review

International Journal of Computer Vision
The Design and Use of Steerable Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Robust recovery of the epipolar geometry for an uncalibrated stereo rig

ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Multiple view geometry in computer vision

Multiple view geometry in computer vision
Non-parametric Local Transforms for Computing Visual Correspondence

ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
A direct method for stereo correspondence based on singular value decomposition

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Wide Baseline Stereo Matching

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of feature points matching between pair of views of the scene is one of the key problems in computer vision, because of the number of applications. In this paper we discuss an alternative version of an SVD matching algorithm earlier proposed in the literature. In the version proposed the original algorithm has been modified for coping with large baselines. The claim of improved performances for larger baselines is supported by experimental evidence.