The human visual spline: Interpolation contours between relatable inducers follow quintic polynomials

Convergence of Quintic Spline Interpolation

Deboor, C. , A Practical Guide to splines, Applied Mathematical Science, Vol. 27 Springer Verlag, New York, 1979. Hall, C. A. and Meyer, W. W. , J. Approximation Theory, 16(1976), pp. 105-122. Howel, G. and Verma, A. K. , Best Error Bond of Quartic Spline Interpolation, J. Appron Theory 58 (1989), 58-67. Davis, P. J. , Interpolation and approximation. New Yrok, 1961. Agrawal, R. P. and Wong, P....

C Quintic Spline Interpolation Over Tetrahedral Partitions

We discuss the implementation of a C quintic superspline method for interpolating scattered data in IR based on a modification of Alfeld’s generalization of the Clough-Tocher scheme described by Lai and LeMéhauté [4]. The method has been implemented in MATLAB, and we test for the accuracy of reproduction on a basis of quintic polynomials. We present numerical evidences that when the partition i...

C 1 Quintic Spline Interpolation over Tetrahedral Partitions

We use C 1 quintic superspline functions to interpolate any given scattered data. The space of C 1 quintic superspline functions is introduced in Lai and LeMehaute'99] and is an improvement of the Alfeld scheme of 3D scattered data interpolation. We have implemented the spline space in MATLAB and tested for the accuracy of reproduction of all quintic polynomials. We present some numerical evide...

Visual interpolation and extrapolation of contours

Learning mixtures of polynomials from data using B-spline interpolation

Hybrid Bayesian networks efficiently encode a joint probability distribution over a set of continuous and discrete variables. Several approaches have been recently proposed for working with hybrid Bayesian networks, e.g., mixtures of truncated basis functions, mixtures of truncated exponentials or mixtures of polynomials (MoPs). We present a method for learning MoP approximations of probability...