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27

3DIM
2005
IEEE

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Further Improving Geometric Fitting

14 years 1 months ago

Further Improving Geometric Fitting

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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.

Ken-ichi Kanatani

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3DIM 2005 | Formal Deﬁnition | Image Processing | KCR Lower Bound | Vision Applications |

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Added	13 Oct 2010
Updated	13 Oct 2010
Type	Conference
Year	2005
Where	3DIM
Authors	Ken-ichi Kanatani

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