Hyper Least Squares and Its Applications

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.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Prasanna Rangarajan, Kenichi Kanatani, Hirotaka Niitsuma, Yasuyuki Sugaya
• Keywords: Computer Vision, ICPR 2010, Large Noise Situations, Order Noise Terms, Rigorous Error Analysis