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We give a formal definition of geometric fitting in a way that suits computer vision applications. We point out that the performance of geometric fitting should be evaluated in the limit of small noise rather than in the limit of a large number of data as recommended in the statistical literature. Taking the KCR lower bound as an optimality requirement and focusing on the linearized constraint case, we compare the accuracy of Kanatani's renormalization with maximum likelihood (ML) approaches including the FNS of Chojnacki et al. and the HEIV of Leedan and Meer. Our analysis reveals the existence of a method superior to all these.
maximum likelihood estimation
Digital Object Identifier: 10.1109/3DIM.2005.49
Published with permission from the copyright holder. This is the institute's copy, as published in 3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on, 13-16 June 2005, Pages 2-13.
Copyright © 2005 IEEE. All rights reserved.
Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
IEEE Computer Society
Fifth International Conference on 3-D Digital Imaging and Modeling
Ottawa, Ontario, Canada