TY - GEN
T1 - Hyper least squares and its applications
AU - Rangarajan, Prasanna
AU - Kanatani, Kenichi
AU - Niitsuma, Hirotaka
AU - Sugaya, Yasuyuki
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78149471943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149471943&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.10
DO - 10.1109/ICPR.2010.10
M3 - Conference contribution
AN - SCOPUS:78149471943
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 5
EP - 8
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -