The non-existence of general-case view-invariants
Geometric invariance in computer vision
Artificial Intelligence - Special volume on computer vision
What can two images tell us about a third one?
International Journal of Computer Vision
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
Efficient Invariant Representations
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Novel View Synthesis by Cascading Trilinear Tensors
IEEE Transactions on Visualization and Computer Graphics
Automatic line matching across views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Using Geometric Constraints for Matching Disparate Stereo Views of 3D Scenes Containing Planes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robot Homing by Exploiting Panoramic Vision
Autonomous Robots
SVD-matching using SIFT features
Graphical Models - Special issue on the vision, video and graphics conference 2005
Piecewise planar scene reconstruction from sparse correspondences
Image and Vision Computing
A new approach to corner matching from image sequence using fuzzy similarity index
Pattern Recognition Letters
| Hi-index | 0.14 |
Many vision tasks rely upon the identification of sets of corresponding features among different images. This paper presents a method that, given some corresponding features in two stereo images, matches them with features extracted from a second stereo pair captured from a distant viewpoint. The proposed method is based on the assumption that the viewed scene contains two planar surfaces and exploits geometric constraints that are imposed by the existence of these planes to first transfer and then match image features between the two stereo pairs. The resulting scheme handles point and line features in a unified manner and is capable of successfully matching features extracted from stereo pairs that are acquired from considerably different viewpoints. Experimental results are presented, which demonstrate that the performance of the proposed method compares favorably to that of epipolar and tensor-based approaches.