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ID 44496
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Author
Rangrajan, Prasanna
Sugaya, Yasuyuki
Abstract
We present a new least squares (LS) estimator, called “HyperLS”, specifically designed for parameter estimation in computer vision applications. It minimizes the algebraic distance under a special scale normalization, which is derived by rigorous error analysis in such a way that statistical bias is removed up to second order noise terms. Numerical experiments suggest that our HyperLS is far superior to the standard LS and comparable in accuracy to maximum likelihood (ML), which is known to produce highly accurate results in image applications but may fail to converge if poorly initialized. Our HyperLS is a perfect candidate for ML initialization. In addition, we discuss how image-based inference problems have different characteristics form conventional statistical applications, with a view to serving as a bridge between mathematicians and computer engineers.
Published Date
2011-01
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Publication Title Alternative
岡山大学工学部紀要
Volume
volume45
Publisher
Faculty of Engineering, Okayama University
Start Page
15
End Page
26
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