All points considered: a maximum likelihood method for motion recovery

Authors:
Daniel Keren;Ilan Shimshoni;Liran Goshen;Michael Werman
Affiliations:
Department of Computer Science, University of Haifa, Haifa, Israel;Faculty of Industrial Engineering, Technion, Technion City, Israel;Faculty of Industrial Engineering, Technion, Technion City, Israel;School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
Venue:
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Year:
2002

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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 non-trivial 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.