Matrix analysis
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Solution of the concave linear complementarity problem
Recent advances in global optimization
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Finding point correspondences in motion sequences preserving affine structure
Computer Vision and Image Understanding
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Reconstruction of 3D-Curves from 2D-Images Using Affine Shape Methods for Curves
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Correspondence and Affine Shape from Two Orthographic Views: Motion and Recognition
Correspondence and Affine Shape from Two Orthographic Views: Motion and Recognition
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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Establishing point correspondences between images is a key step for 3D-shape computation. Nevertheless, shape extraction and point correspondence are treated, usually, as two different computational processes. We propose a new method for solving the correspondence problem between points of a fully uncalibrated scaled-orthographic image sequence. Among all possible point selections and permutations, our method chooses the one that minimizes the fourth singular value of the observation matrix in the factorization method. This way, correspondences are set such that shape and motion computation are optimal. Furthermore, we show this is an optimal criterion under bounded noise conditions.Also, our formulation takes feature selection and outlier rejection into account, in a compact and integrated way. The resulting combinatorial problem is cast as a concave minimization problem that can be efficiently solved. Experiments show the practical validity of the assumptions and the overall performance of the method.