smistad/FLTK-OpenGL-OpenCL-Interoperability
Testing FLTK, OpenGL and OpenCL Interoperability
Discovered public repositories for smistad in the GitHub catalog.
Testing FLTK, OpenGL and OpenCL Interoperability
A simple test for having multiple windows with the same OpenGL context using GLUT
Public repository.
A test script to see if it is possible to detect which GPU is connected to the screen using OpenCL. The purpose of this is to use the GPU connected to screen for visualizations and the others for computation only.
A GPU implementation of a full multgrid solver of Gradient Vector Flow (GVF) using OpenCL
C++ implementation of Gradient Vector Flow
Public repository.
Parallel/GPU level set volume segmentation using OpenCL
Vascular Tree Synthesis Software. You will need CMake and ITK to run VascuSynth. Read the paper in the Insight Journal for instructions.
Creating a memory mapped file using the boost iostrames library
Implementing the Automatic Generation of 3D Statistical Shape Models with ITK
A software for fast segmentation and centerline extraction of tubular structures (e.g. blood vessels and airways) from different modalities and organs using GPUs and OpenCL
Just a simple test of the google C++ unit test framework (GTest)
This is an example of how to use the Simple Image Processing Library (see www.github.com/smistad/SIPL/).
An example of Gaussian blur using OpenCL and the built-in Images/textures
The Simple Image Processing Library (SIPL) is a C++ library with the main goal of making it easy to go from an algorithm concept to pictures on the screen.
A small "getting started" tutorial for OpenCL. See http://www.eriksmistad.no/getting-started-with-opencl-and-gpu-computing/ for more info
A small set of function based on the OpenCL C++ bindings to help set up an OpenCL and OpenCL-GL context as well as compiling OpenCL code
A GPU implementation of the Marching Cubes algorithm for extracting surfaces from volumes using OpenCL and OpenGL
An optimized OpenCL implementation of Gradient Vector Flow (GVF) that runs on GPUs and CPUs for both 2D and 3D. For more details about the implementation, see the scientific article Real-time gradient vector flow on GPUs using OpenCL http://www.springerlink.com/content/v0071r27706u5135/
This is an implementation of Gradient Vector Flow (GVF) for 3D in Matlab. It is based on the original 2D implementation of Xu and Prince.