Multivariate interpolation Information & Multivariate interpolation Links at HealthHaven.com
advertise
add site
services
publishers
database
health videos
Bookmark and Share

search wiki for    ?
web dir firms image gallery news pdf wiki shop video 
about
toolbar
stats
live show
health store
more stuff
JOIN/LOGIN
Featured Results:
 Multivariate CART for survival with dental applicaions
Multivariate CART for survival with dental applicaions
researchresources.bumc.bu...
  Multivariate Mixture Distribution Model to Analyse Twin Data of Unknown...
Multivariate Mixture Distribution Model to Analyse Twin Data of Unknown...
ichg2006.com
 Endovascular Aortic Repair: A Multivariate ...
Endovascular Aortic Repair: A Multivariate...
westernthoracic.org
 A comparison of regression trees, logistic regression, generalized...
A comparison of regression trees, logistic regression, generalized...
ices.on.ca
 

In numerical analysis, multivariate interpolation or spatial interpolation is interpolation on functions of more than one variable.

The function to be interpolated is known at given points (x_i, y_i, z_i, \dots) and the interpolation problem consist of yielding values at arbitrary points (x,y,z,\dots).

Contents

[edit] Uniform grid

For function values known on a prescribed uniform grid, the following methods are available

[edit] 2 dimensions

Bitmap resampling is the application of 2D multivariate interpolation in image processing.

Three of the methods applied on the same dataset, from 16 values located at the black dots. The colours represent the interpolated values.

See also Padua points, for polynomial interpolation in two variables.

[edit] 3 dimensions

See also bitmap resampling.

[edit] Tensor product splines for N dimensions

Catmull-Rom splines can be easily generalized to any number of dimensions. The cubic Hermite spline article will remind you that \mathrm{CINT}_x(f_{-1}, f_0, f_1, f_2) = \mathbf{b}(x) \cdot \left( f_{-1} f_0 f_1 f_2 \right) for some 4-vector \mathbf{b}(x) which is a function of x alone, where fj is the value at j of the function to be interpolated. Rewrite this approximation as

 \mathrm{CR}(x) = \sum_{i=-1}^2 f_i b_i(x)

This formula can be directly generalized to N dimensions [1]:

 \mathrm{CR}(x_1,\dots,x_N) = \sum_{i_1,\dots,i_N=-1}^2 f_{i_1\dots i_N} \prod_{j=1}^N b_{i_j}(x_j)

Note that similar generalizations can be made for other types of spline interpolations, including Hermite splines. In regards to efficiency, the general formula can in fact be computed as a composition of successive CINT-type operations for any type of tensor product splines, as explained in the tricubic interpolation article. However, the fact remains that if there are n terms in the 1-dimensional CR-like summation, then there will be nN terms in the N-dimensional summation.

[edit] Non-uniform grid

Schemes defined on a non-uniform grid should all work on a uniform grid, typically reducing to another known method.

[edit] Notes

  1. ^ Two hierarchies of spline interpolations. Practical algorithms for multivariate higher order splines

[edit] External links





Product Results (view all...)

search wiki for    ?
web dir firms image gallery news pdf wiki shop video 



↑ top of page ↑about thumbshots