Abstract
This paper is focused on error characterization of the factorization approach to shape and motion recovery from image sequence using results from matrix perturbation theory and covariance propagation for linear models. Given the 2-D projections of a set of points across multiple image frames and small perturbation on image coordinates, first order perturbation and covariance matrices for 3-D affine/Euclidean shape and motion are derived and validated with the ground truth. The propagation of the small perturbation and covariance matrix provides better understanding of the factorization approach and its results, provides error sensitivity information for 3-D affine/Euclidean shape and moton subject to small image error. Experimental results are demonstrated to support the analysis and show how the error analysis and error measures can be used.
This work is supported by Siemens Corporate Research, Princeton, NJ 08540
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© 2000 Springer-Verlag Berlin Heidelberg
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Sun, Z., Ramesh, V., Tekalp, A.M. (2000). Error Characterization of the Factorization Approach to Shape and Motion Recovery. In: Triggs, B., Zisserman, A., Szeliski, R. (eds) Vision Algorithms: Theory and Practice. IWVA 1999. Lecture Notes in Computer Science, vol 1883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44480-7_14
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DOI: https://doi.org/10.1007/3-540-44480-7_14
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