Abstract
We discuss here maximum likelihood (ML) estimation and Sampson error minimization in the general mathematical framework of the preceding chapter. We first derive the Sampson error as a first approximation to the Mahalanobis distance (a generalization of the geometric distance or the reprojection error) of ML. Then we do high-order error analysis to derive explicit expressions for the covariance and bias of the solution. The hyperaccurate correction procedure is derived in this framework.
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References
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K. Kanatani, Y. Sugaya, Hyperaccurate correction of maximum likelihood for geometric estimation. IPSJ Trans. Comput. Vis. Appl. 5, 19–29 (2013)
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Kanatani, K., Sugaya, Y., Kanazawa, Y. (2016). Maximum Likelihood of Geometric Estimation. In: Guide to 3D Vision Computation. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-48493-8_15
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DOI: https://doi.org/10.1007/978-3-319-48493-8_15
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-48493-8
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