Hyper least squares and its applications

Prasanna Rangarajan, Kenichi Kanatani, Hirotaka Niitsuma, Yasuyuki Sugaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We present a new form of least squares (LS), called "hyperLS", 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.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages5-8
Number of pages4
DOIs
Publication statusPublished - Nov 18 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Rangarajan, P., Kanatani, K., Niitsuma, H., & Sugaya, Y. (2010). Hyper least squares and its applications. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 5-8). [5597662] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.10