Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Affine analysis of image sequences
Affine analysis of image sequences
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Ambiguities in Structure From Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sequential Factorization Method for Recovering Shape and Motion From Image Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error characterization of the factorization method
Computer Vision and Image Understanding
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
Uncertainty Modeling for Optimal Structure from Motion
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
A Factorization Method for Affine Structure from Line Correspondences
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Rank 1 Weighted Factorization for 3D Structure Recovery: Algorithms and Performance Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose uncertainty analysis using geometrical property between 2D-to-3D under affine reconstruction. In situations when are no missing data in an observation matrix, the accurate solution is known to be provided by Singular Value Decomposition (SVD). However, when converting image sequences to 3D, several entries of the matrix have not been observed and other entries have been perturbed by the influence of noise. In this case, there is no simple solution. In this paper, a new approach is applied for recovering missing data using geometrical properties between a 2D image plane and 3D shape and for estimating noise level in an observation matrix using ranks of SVD. This paper consists of four main phases: geometrical properties between 2D image plane and 3D error space, geometrical recovering of missing data, and noise level estimation in the observation matrix.