Several factorization techniques have been proposed for tackling the Structure from Motion problem. Most of them provide a good solution, while the amount of missing data is within an acceptable ratio. Focussing on this problem, we propose an incremental multiresolution scheme able to deal with a high rate of missing data, as well as noisy data. It is based on an iterative approach that applies a classical factorization technique in an incrementally reduced space. Information recovered following a coarse-to-fine strategy is used for both, filling in the missing entries of the input matrix and denoising original data. A statistical study of the proposed scheme compared to a classical factorization technique is given. Experimental results obtained with synthetic data and real video sequences are presented to demonstrate the viability of the proposed approach. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Julià, C., Sappa, A., Lumbreras, F., Serrat, J., & López, A. (2006). Factorization with missing and noisy data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3991 LNCS-I, pp. 555–562). Springer Verlag. https://doi.org/10.1007/11758501_75
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