Image‐guided blended neighbor interpolation of scattered data

2009 ◽  
Author(s):  
Dave Hale
Keyword(s):  
Scattered Data ◽  
Image Guided ◽  
Interpolation Of Scattered Data

Interpolation of scattered data on closed surfaces

1990 ◽  
Vol 7 (1-4) ◽  
pp. 303-312 ◽  
Author(s):  
Thomas A. Foley ◽  
David A. Lane ◽  
Gregory M. Nielson ◽  
Richard Franke ◽  
Hans Hagen
Keyword(s):  
Scattered Data ◽  
Closed Surfaces ◽  
Interpolation Of Scattered Data

Powell-Sabin splines in range restricted interpolation of scattered data

Computing ◽  
1994 ◽  
Vol 53 (2) ◽  
pp. 137-154 ◽  
Author(s):  
B. Mulansky ◽  
J. W. Schmidt
Keyword(s):  
Scattered Data ◽  
Interpolation Of Scattered Data

Multilevel interpolation of scattered data using ${\mathcal{H}}$-matrices

2020 ◽  
Vol 85 (4) ◽  
pp. 1175-1193
Author(s):  
Sabine Le Borne ◽  
Michael Wende
Keyword(s):  
Scattered Data ◽  
Interpolation Of Scattered Data

C1 Convexity-Preserving Interpolation of Scattered Data

1999 ◽  
Vol 20 (5) ◽  
pp. 1732-1752 ◽  
Author(s):  
N. K. Leung ◽  
R. J. Renka
Keyword(s):  
Scattered Data ◽  
Interpolation Of Scattered Data

Algorithms for convexity preserving interpolation of scattered data

1996 ◽  
pp. 175-184
Author(s):  
J. M. Carnicer ◽  
Mikael S. Floater
Keyword(s):  
Scattered Data ◽  
Interpolation Of Scattered Data

A New Fast Iterative Method for Interpolation of Multivariate Scattered Data

2005 ◽  
Vol 5 (3) ◽  
pp. 276-293 ◽  
Author(s):  
Andrey V. Masjukov ◽  
Vladimir I. Masjukov
Keyword(s):  
Response Function ◽  
Scattered Data ◽  
Convergence Result ◽  
Iteration Step ◽  
Tuning Parameter ◽  
Sinc Function ◽  
Piecewise Constant Function ◽  
The Stability ◽  
Interpolation Of Scattered Data ◽  
Current Approximation

AbstractThis paper presents Iterative Scalable Smoothing (ISS), a new itera- tive multi-scale method for multivariate interpolation of scattered data. Each iteration step in the process reduces the residues of the current interpolation result by appli- cation of a smoothing operator to a piecewise constant function that interpolates the residues of the current interpolant, and by adding the resulting function to the current approximation, which is initially set to zero. The convergence of the method is proved and conditions for the di®erentiability of the convergence result are given. For a uni- form mesh an e±cient algorithm is constructed, for which the numerical complexity is estimated. Several 2D numerical examples illustrate the theoretical results. By a 3D test with several test-functions and random nodes it is shown that the accuracy of the proposed method is comparable with the quadratic modiffcation of Shepard's method, which is known to be more accurate than triangle-based methods. Then, in 1D tests, by stochastic simulation with random nodes and random functions the ISS method is compared with the Cubic Splines method, Shepard's method and the Kriging method. We also compare the stability of these methods with respect to noisy data. For the special case of regular nodes, properties of the method are verified by comparing its 1D response function with the response function of the cubic spline and the perfect interpolator (the sinc-function). Special attention is paid to the e®ect of the tuning parameter

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