Joint SVD and its application to factorization method

Authors:
Gen Hori
Affiliations:
Faculty of Business Administration, Asia University, Tokyo, Japan and Lab. for Advanced Brain Signal Processing, BSI, RIKEN, Saitama, Japan
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 4
Cited 0

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A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation

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Comparison of Two Main Approaches to Joint SVD

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Non-orthogonal joint diagonalization in the least-squares sensewith application in blind source separation

IEEE Transactions on Signal Processing

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Abstract

This paper introduces an application of joint SVD to the factorization method which is a standard method in computer vision for the estimation of the camera motion and the object shape from an image stream taken by a camera that moves around the object. For computer vision systems with several cameras installed for the same direction, we implement a new algorithm for estimation of camera motion matrix that utilizes the measurement matrices from all the cameras based on joint SVD.