14 DAY TRIAL // NVIDIA seems to be getting quite busy at its GPU Technology Conference (GTC), having revealed, more or less, not just the roadmap for its future GPUs and Tegra products, but also upgrading certain existing technologies, such as the CUDA.
What NVIDIA just revealed is that the CUDA parallel computing architecture will soon support OpenCV.
Open CV is a solution used to develop applications for such fields as the medical, manufacturing, security, consumer, robotics and automotive.
"NVIDIA GPU acceleration of OpenCV now supplies the computational power for the sophisticated algorithms needed for advanced automotive driver assistance applications, and other popular consumer applications," said Taner Ozcelik, general manager of NVIDIA's automotive business.
"OpenCV gives developers the toolbox they need to quickly unleash this power for research and development of these products without needing to recreate vision algorithms from scratch,” he added.
“This is a key milestone that could usher in a significant increase in the use of Computer Vision across a broad range of industries," the general manager went on to saying.
"Computational power in Computer Vision has been a limiting factor not only for the use of recent powerful algorithms in object recognition, tracking and 3D reconstruction, but also has limited the creativity of algorithms people are willing to invent," said Gary Bradski, senior researcher at Willow Garage, and founder of OpenCV.
"With CUDA GPU acceleration, many OpenCV algorithms will run five to ten times faster, making current algorithms more practical for application developers and allowing the invention and combination of more capable applications in the future," he concluded.
This support is expected to become available during the spring of 2011, but testing GPU-accelerated functions can already be tested with the existing OpenCV source code repository.
"My research lab uses OpenCV extensively in our autonomous vehicles," said Sebastian Thrun, professor of computer science and electrical engineering at Stanford University. "CUDA GPU acceleration for OpenCV provides my research team an instant performance bump which is critical in our research,” he explained.