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Abstract: When interpolating incomplete data, one can choose a parametric model, or opt for a more general approach and use a non-parametric model which allows a very large class of interpolants. A popular non-parametric model for interpolating various types of data is based on regularization, which looks for an interpolant that is both close to the data and also "smooth" in some sense. Formally, this interpolant is obtained by minimizing an error functional which is the weighted sum of a "fidelity term" ... (Update)
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...derivative is minimized additionally than the solution is a cubic spline. For details about spline smoothing and related ideas see [6, 7, 8]. Our procedure is as follows: given the number n , the position of the design points , and the respective value of the image at...
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BibTeX entry: (Update)
D. Keren and M. Werman, "A full bayesian approach to curve and surface reconstruction, " in Maximum Entropy and Bayesian Methods 1995, R. Silver and K. Hanson, eds., Kluwer Academic, Dordrecht, to appear. http://citeseer.ist.psu.edu/keren99full.html More
@article{ keren99full,
author = "D. Keren and M. Werman",
title = "A full {Bayesian} approach to curve and surface reconstruction",
journal = "J. Math. Imaging and Vision",
volume = "11",
pages = "27--43",
year = "1999",
url = "citeseer.ist.psu.edu/keren99full.html" }
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