Software Listing of Author : "Gabriel Peyre"

First keep in mind that this is not a MATLAB tutorial. This is just a list of tricks I have found useful while writing my toolboxes available on the MATLAB Central repository. You can e-mail me if you have corrections about these pieces of code, or if you would like to add your own tips to those described in this document. Some random examples (you will find piece of code in the pdf) : - Access a matrix from a list of entries. - Output a string without carriage return. - Draw a 3D sphere. - Remove the ticks from a drawing. - Compute centered finite differences. - Generate n points sampled uniformly at random on a sphere. - Compute the 3D rotation matrix M around an axis v. - Display the result of an FFT with the 0 frequency in the middle. - Perform a symmetric boundary adapted convolution. - Draw the image of a disk and a square. -...

  • Platform: Matlab, Scripts

Given an input primal gear, the toolbox compute its associated dual gear together with the associated distance between the gear centers. The gears are described as a discretized polar curve. The dual gear is solved by a numerical integration of the non-sliding condition. The distance between the centers is bound using a dichotomic search to ensure the same periodicity for the two gears.

  • Platform: Matlab, Scripts

The Alpert transform is a multiwavelets transform based on orthogonal polynomials. It was originally designed for the resolution of partial differential and integral equations, since it avoids boundary artifact and can be used with an arbitrary sampling. The reference for the numerical algorithm: Bradley K. Alpert, Wavelets and Other Bases for Fast Numerical Linear Algebra, in Wavelets: A Tutorial in Theory and Applications, C. K. Chui, editor, Academic Press, New York, 1992. And more theoretical (continuous setting): B.K. Alpert, A class of bases in L^2 for the sparse representatiion of integral operators, in SIAM J. Math. Anal., 24 (1993), 246-262. The strengh of this transform is that you can transform data sampled irregularly. Of course this algorithm runs in linear time, i.e. O(n). The use of multiwavelets remove any boundary...

  • Platform: Matlab, Scripts

See HTML help file for information about the content (see below in 'Published HTML files'). Copyright (c) 2008 Gabriel Peyre

  • Platform: Matlab, Scripts

The Fast Marching algorithm, introduced by Sethian (1996) is a numerical algorithm that is able to catch the viscosity solution of the Eikonal equation |grad(D)|=P. The level set {x F(x)=t} can be seen as a front advancing with speed P(x). The resulting function D is a distance function, and if the speed P is constant, it can be seen as the distance function to a set of starting points. The Fast Marching is very similar to the Dijkstra algorithm that finds shortest paths on graphs. Using a gradient descent of the distance function D, one is able to extract a good approximation of the shortest path (geodesic) in various settings (euclidean for P constant, and a weighted riemanian manifold with P varying). The main reference about the Fast Marching algorithm is the book Level Set Methods and Fast Marching Methods Evolving...

  • Platform: Matlab, Scripts

toobox_signal - image processing related functions. This toolbox contains functions related to image processing, including * images loading and generation. * filtering and blurring functions. * anisotropic and spacially variant filters. * non-linear filtering (median, LIC, bilateral). * High Dynamic Range image loading and equalization. * total variation image processing (denoising and decomposition). * non-wavelets image transforms such as local DCT. * Various helpers functions. Copyright (c) 2007 Gabriel Peyre

  • Platform: Matlab, Scripts

toobox_signal - signal processing related functions. This toolbox contains functions related to image processing, including * images loading and generation. * filtering and blurring functions. * anisotropic and spacially variant filters. * non-linear filtering (median, LIC, bilateral). * High Dynamic Range image loading and equalization. * total variation image processing (denoising and decomposition). * non-wavelets image transforms such as local DCT. * Various helpers functions. Copyright (c) 2007 Gabriel Peyre

  • Platform: Matlab, Scripts
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