Generalizing the Prediction Sum of Squares Statistic and Formula, Application to Linear Fractional Image Warp and Surface Fitting-Reference-Cited by-同舟云学术

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Generalizing the Prediction Sum of Squares Statistic and Formula, Application to Linear Fractional Image Warp and Surface Fitting

Published:2016-09-20 Issue:1 Volume:122 Page:61-83
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Bartoli Adrien

Funder

European Research Council

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-016-0954-x.pdf

Reference37 articles.

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automation and Control, AC–19(6), 716–723.

2. Allen, D. M. (1971). Mean square error of prediction as a criterion for selecting variables. Technometrics, 13(3), 469–475.

3. Bartoli, A. (2008). Maximizing the predictivity of smooth deformable image warps through cross-validation. Journal of Mathematical Imaging and Vision, 31(2–3), 133–145.

4. Bartoli, A. (2009). On computing the prediction sum of squares statistic in linear least squares problems with multiple parameter or measurement sets. International Journal of Computer Vision, 85(2), 133–142.

5. Bartoli, A., Perriollat, M., & Chambon, S. (2010). Generalized thin-plate spline warps. International Journal of Computer Vision, 88(1), 85–110.

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1. Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision;2019 IEEE/CVF International Conference on Computer Vision (ICCV);2019-10

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