Uncertainty Analysis Using Geometrical Property Between 2D-to-3D Under Affine Projection
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.
KeywordsNoise Level Geometrical Property Singular Value Decomposition Factorization Method Camera Motion
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