Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Factorization with Uncertainty
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
3D Reconstruction by Fitting Low-Rank Matrices with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Optimal Point Correspondence through the Use of Rank Constraints
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
| Hi-index | 0.00 |
Establishing correspondence between features of a set of images has been a long-standing issue amongst the computer vision community. We propose a method that solves the multi-frame correspondence problem by imposing a rank constraint on the observed scene, i.e. rigidity is assumed. Since our algorithm is based solely on a geometrical (global) criterion, it does not suffer from issues usually associated to local methods, such as the aperture problem. We model feature matching by introducing the assignment tensor, which allows simultaneous feature alignment for all images, thus providing a coherent solution to the calibrated multi-frame correspondence problem in a single step of linear complexity. Also, an iterative method is presented that is able to cope with the non-calibrated case. Moreover, our method is able to seamlessly reject a large number of outliers in every image, thus also handling occlusion in an integrated manner.