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Motion estimation via dynamic vision

Soatto, Stefano and Frezza, Ruggero and Perona, Pietro (1996) Motion estimation via dynamic vision. IEEE Transactions on Automatic Control, 41 (3). pp. 393-413. ISSN 0018-9286. doi:10.1109/9.486640.

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Estimating the three-dimensional motion of an object from a sequence of projections is of paramount importance in a variety of applications in control and robotics, such as autonomous navigation, manipulation, servo, tracking, docking, planning, and surveillance. Although “visual motion estimation” is an old problem (the first formulations date back to the beginning of the century), only recently have tools from nonlinear systems estimation theory hinted at acceptable solutions. In this paper the authors formulate the visual motion estimation problem in terms of identification of nonlinear implicit systems with parameters on a topological manifold and propose a dynamic solution either in the local coordinates or in the embedding space of the parameter manifold. Such a formulation has structural advantages over previous recursive schemes, since the estimation of motion is decoupled from the estimation of the structure of the object being viewed, and therefore it is possible to handle occlusions in a principled way.

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Perona, Pietro0000-0002-7583-5809
Additional Information:© Copyright 1996 IEEE. Reprinted with permission. Manuscript received February 17, 1995. Recommended by Associate Editor, A. J. van der Schaft. This work was supported in part by the California Institute of Technology, a fellowship from the University of Padova, a fellowship from the "A. Gini" Foundation, an AT&T Foundation Special Purpose grant, ONR Grant N0014-93-1-0990, and grant ASI-RS-103 from the Italian Space Agency. The authors wish to thank Prof. G. Picci for his advice and encouragement, Prof. K. Astrom for his discussions on implicit Kalman filtering, Prof. R. Murray, and Prof. S. Sastry for observations and useful suggestions. Finally, the authors thank J. Oliensis and I. Thomas for providing the rocket sequence.
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Deposited On:11 Dec 2006
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