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ID 49320
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Abstract
We summarize techniques for optimal geometric estimation from noisy observations for computer vision applications. We first discuss the interpretation of optimality and point out that geometric estimation is different from the standard statistical estimation. We also describe our noise modeling and a theoretical accuracy limit called the KCR lower bound. Then, we formulate estimation techniques based on minimization of a given cost function: least squares (LS), maximum likelihood (ML), which includes reprojection error minimization as a special case, and Sampson error minimization. We describe bundle adjustment and the FNS scheme for numerically solving them and the hyperaccurate correction that improves the accuracy of ML. Next, we formulate estimation techniques not based on minimization of any cost function: iterative reweight, renormalization, and hyper-renormalization. Finally, we show numerical examples to demonstrate that hyper-renormalization has higher accuracy than ML, which has widely been regarded as the most accurate method of all. We conclude that hyper-renormalization is robust to noise and currently is the best method.
Published Date
2013-01
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Publication Title Alternative
岡山大学工学部紀要
Volume
volume47
Publisher
Faculty of Engineering, Okayama University
Start Page
1
End Page
18
ISSN
1349-6115
NCID
AA12014085
Content Type
Departmental Bulletin Paper
language
英語
Copyright Holders
Copyright © by the authors
File Version
publisher
Refereed
False
Eprints Journal Name
mfe