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All Points Considered: A Maximum Likelihood Method for Motion Recovery

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Geometry, Morphology, and Computational Imaging

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2616))

Abstract

This paper addresses the problem of motion parameter recovery. A novel paradigm is offered to this problem, which computes a maximum likelihood (ML) estimate. The main novelty is that all domain-range point combinations are considered, as opposed to a single “optimal” combination. While this involves the optimization of nontrivial cost functions, the results are superior to those of the so-called algebraic and geometric methods, especially under the presence of strong noise, or when the measurement points approach a degenerate configuration.

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© 2003 Springer-Verlag Berlin Heidelberg

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Keren, D., Shimshoni, I., Goshen, L., Werman, M. (2003). All Points Considered: A Maximum Likelihood Method for Motion Recovery. In: Asano, T., Klette, R., Ronse, C. (eds) Geometry, Morphology, and Computational Imaging. Lecture Notes in Computer Science, vol 2616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36586-9_5

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  • DOI: https://doi.org/10.1007/3-540-36586-9_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00916-0

  • Online ISBN: 978-3-540-36586-0

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