Referenced Items are not available for this document.
We present a new form of least squares (LS), called "hyper LS", for geometric problems that frequently appear in computer vision applications. Doing rigorous error analysis, we maximize the accuracy by introducing a normalization that eliminates statistical bias up to second order noise terms. Our method yields a solution comparable to maximum likelihood (ML) without iterations, even in large noise situations where ML computation fails.
Published in:
Pattern Recognition (ICPR), 2010 20th International Conference on
Date of Conference: 23-26 Aug. 2010
- Page(s):
- 5 - 8
- ISSN :
- 1051-4651
- Print ISBN:
- 978-1-4244-7542-1
- INSPEC Accession Number:
- 11577951
- Conference Location :
- Istanbul
- Digital Object Identifier :
- 10.1109/ICPR.2010.10
- Product Type:
- Conference Publications
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