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.
|Original language||American English|
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Editors||Tetsuo Asano, Reinhard Klette, Chrisitan Ronse|
|Number of pages||14|
|ISBN (Electronic)||3540009167, 9783540009160|
|State||Published - 2003|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|