ID | 44496 |
JaLCDOI | |
Sort Key | 3
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FullText URL | |
Author |
Rangrajan, Prasanna
Sugaya, Yasuyuki
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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.
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Publication Title |
Memoirs of the Faculty of Engineering, Okayama University
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Published Date | 2011-01
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Volume | volume45
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Publisher | Faculty of Engineering, Okayama University
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Start Page | 15
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End Page | 26
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ISSN | 1349-6115
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NCID | AA12014085
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Content Type |
Departmental Bulletin Paper
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OAI-PMH Set |
岡山大学
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language |
English
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Copyright Holders | Copyright © by the authors
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File Version | publisher
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Refereed |
False
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NAID | |
Eprints Journal Name | mfe
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