Analysis of 3-D rotation fitting

Computational techniques for fitting a 3-D rotation to 3-D data are recapitulated in a refined form as minimization over proper rotations, extending three existing methods-the method of singular value decomposition, the method of polar decomposition, and the method of quaternion representation. Then...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 16; no. 5; pp. 543 - 549
Main Author Kanatani, K.
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.05.1994
IEEE Computer Society
Subjects
Algorithm design and analysis
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Covariance matrix
Exact sciences and technology
Fitting
Game theory
Labeling
Learning automata
Matrix decomposition
Motion estimation
Pattern recognition. Digital image processing. Computational geometry
Quaternions
Singular value decomposition
Computer vision
Covariance matrix
3D rotation
Polar decomposition
Essential matrix
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Summary:Computational techniques for fitting a 3-D rotation to 3-D data are recapitulated in a refined form as minimization over proper rotations, extending three existing methods-the method of singular value decomposition, the method of polar decomposition, and the method of quaternion representation. Then, we describe the problem of 3-D motion estimation in this new light. Finally, we define the covariance matrix of a rotation and analyze the statistical behavior of errors in 3-D rotation fitting.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.291441