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Dynamic cubic splines with JSXGraph; Lectures on the theory and practice of spline interpolation; Paper which explains step by step how cubic spline interpolation is done, but only for equidistant knots. Numerical Recipes in C, Go to Chapter 3 Section 3-3; A note on cubic splines; Information about spline interpolation (including code in ...
Cubic polynomial splines are extensively used in computer graphics and geometric modeling to obtain curves or motion trajectories that pass through specified points of the plane or three-dimensional space. In these applications, each coordinate of the plane or space is separately interpolated by a cubic spline function of a separate parameter t.
Example showing non-monotone cubic interpolation (in red) and monotone cubic interpolation (in blue) of a monotone data set. Monotone interpolation can be accomplished using cubic Hermite spline with the tangents m i {\displaystyle m_{i}} modified to ensure the monotonicity of the resulting Hermite spline.
Bicubic interpolation can be accomplished using either Lagrange polynomials, cubic splines, or cubic convolution algorithm. In image processing , bicubic interpolation is often chosen over bilinear or nearest-neighbor interpolation in image resampling , when speed is not an issue.
If a type of spline has additional linear conditions imposed upon it, then the resulting spline will lie in a subspace. The space of all natural cubic splines, for instance, is a subspace of the space of all cubic C 2 splines. The literature of splines is replete with names for special types of splines. These names have been associated with:
Example C++ code for several 1D, 2D and 3D spline interpolations (including Catmull-Rom splines). Multi-dimensional Hermite Interpolation and Approximation, Prof. Chandrajit Bajaja, Purdue University; Python library containing 3D and 4D spline interpolation methods.
In computer graphics, the centripetal Catmull–Rom spline is a variant form of the Catmull–Rom spline, originally formulated by Edwin Catmull and Raphael Rom, [1] which can be evaluated using a recursive algorithm proposed by Barry and Goldman. [2]
SciPy: Python-library, contains a sub-library scipy.interpolate with spline functions based on FITPACK; TinySpline: C-library for splines with a C++ wrapper and bindings for C#, Java, Lua, PHP, Python, and Ruby; Einspline: C-library for splines in 1, 2, and 3 dimensions with Fortran wrappers