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Hyper Least Squares and Its Applications

By Prasanna Rangarajan, Kenichi Kanatani, Hirotaka Niitsuma and Yasuyuki Sugaya


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.

Year: 2013
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