@inbook{04ef802c672a490aa61ddd3dcc4f73d4,
title = "All points considered: A maximum likelihood method for motion recovery",
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.",
author = "Daniel Keren and Ilan Shimshoni and Liran Goshen and Michael Werman",
year = "2003",
doi = "10.1007/3-540-36586-9_5",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "72--85",
editor = "Tetsuo Asano and Reinhard Klette and Chrisitan Ronse",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}