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MCX is a GPU-accelerated, general-purpose, physically-accurate and feature-rich 3-D light transport simulator. It is one of the fastest simulators because it can use tens of thousands of GPU threads to simulate photons in parallel.
This project, initially developed at University College London, contains programs to perform rigid, affine and non-linear registration of nifti or analyse images. Two versions of the algorithms are included, a CPU- and a GPU- (using CUDA) based implementation.
This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
This is a standalone, all in one Edge Detector that is mainly running on GPU using the CUDA technology. Each algorithm also has an OpenCV counterpart which is the closest one in terms of speed.
This project is partly based on the Sobel Filter sample available as part of the CUDA SDK.
This project also partly uses the OpenCV library to load different types of image.
This program includes: Prewitt filter, Roberts Cross, Canny edge detector and finally Sobel filter with the ability to modify some variables such as sigma in Canny and see the result in real-time.
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CUDA accelerated spherical model forward solution for EEG/MEG
CUDA-SPHERE-FWD-MEEG is a CUDA C based toolkit which provides a GPU based implementation of the spherical model forward solution for the 306 channel Elekta Neuromag MEG system and the EEG. The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor locations. ...