TY - CHAP
T1 - All points considered
T2 - A maximum likelihood method for motion recovery
AU - Keren, Daniel
AU - Shimshoni, Ilan
AU - Goshen, Liran
AU - Werman, Michael
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=21144447099&partnerID=8YFLogxK
U2 - 10.1007/3-540-36586-9_5
DO - 10.1007/3-540-36586-9_5
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AN - SCOPUS:21144447099
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 85
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Asano, Tetsuo
A2 - Klette, Reinhard
A2 - Ronse, Chrisitan
PB - Springer Verlag
ER -