OpenVIDIA projects implement computer vision algorithms running on on graphics hardware such as single or multiple graphics processing units(GPUs) using OpenGL, Cg and CUDA-C. Some samples will soon support OpenCL and Direct Compute API's also.
An active project within OpenVida, CVWB (CUDA Vision Workbench) includes a back-end DLL and front-end Windows application that run common image processing routines in a framework convenient for interactive experimentation. Additional OpenVidia projects for Stereo Vision, Optical Flow and Feature Tracking algorithms are detailed below.
OpenVIDIA projects utilize the computational power of the GPU to provide real--time computer vision and imaging much faster than the CPU is capable of, while offloading the CPU to allow it to conduct concurrent tasks and also using less power.
This project was founded at the Eyetap Personal Imaging Lab (ePi Lab) at the Electrical and Computer Engineering Group at the University of Toronto. It has been expanded to include contributions from many sources in academia and industry.