All points considered: A maximum likelihood method for motion recovery

Daniel Keren*, Ilan Shimshoni, Liran Goshen, Michael Werman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTetsuo Asano, Reinhard Klette, Chrisitan Ronse
PublisherSpringer Verlag
Pages72-85
Number of pages14
ISBN (Electronic)3540009167, 9783540009160
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2616
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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