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Friday, April 1, 2016
OpenVX: A Framework for Accelerating Computer Vision [Tutorial]
This event is hosted/sponsored by IEEE SPS Chapter.
Speakers:
Kari Pulli, Intel
Radhakrishna Giduthuri, AMD
Thierry Lepley, NVIDIA
Location:
AMD Commons C-6/7/8, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)
Schedule:
2:00pm-2:30pm: Checkin
2:30pm-6:00pm: Tutorial
Cost:
$10 for IEEE members with SPS membership
$30 for IEEE members
$50 for Others
Did you miss this event? -- Click here to check for other LOCAL OpenVX Tutorial events.
Abstract:
OpenVX is a royalty-free open standard API released by the Khronos Group in 2014. OpenVX enables performance and power-optimized computer vision functionality, especially important in embedded and real-time use cases. The course covers both the function-based API and the graph API that enable OpenVX developers to efficiently run computer vision algorithms on heterogeneous computing architectures. A set of example algorithms from computational photography and advanced driver assistance mapped to the graph API will be discussed. Also covered is the relationship between OpenVX and OpenCV, as well as OpenCL. The tutorial includes hands-on practice session that gets the participants started on solving real computer vision problems using OpenVX.
Learning Outcomes:
Understanding the architecture of OpenVX computer vision API, its relation to OpenCV, OpenGL, and OpenCL APIs; getting fluent in actually using OpenVX for real-time image processing and computer vision tasks.
Biography:
Kari Pulli is Sr. Principal Engineer at Intel. Earlier, he was VP of Computational Imaging at Light, Sr. Director of Research at NVIDIA, and Nokia Fellow at Nokia Research center, working on Mobile Visual Computing. Kari has a long background in standardization and at Khronos he has contributed to many mobile media standards including OpenVX. He is a frequent author and speaker at venues like CVPR and SIGGRAPH, with h-index of 27. Kari has a PhD from University of Washington, MBA from University of Oulu, and has taught and worked as a researcher at University of Oulu, Stanford University, and MIT.
Radhakrishna Giduthuri is a Design Engineer at Advanced Micro Devices (AMD) focusing on development of computer vision toolkit and libraries for heterogeneous compute platforms. He has extensive background with software design and performance tuning for various computer architectures ranging from General Purpose DSPs, Customizable DSPs, Media Processors, Heterogeneous Processors, GPUs, and several CPUs. He is a member of Khronos OpenVX working group representing AMD. In the past he was a member of SMPTE video compression standardizing committee for several years. He is also chair of IEEE Signal Processing Society Chapter of Santa Clara Valley.
Thierry Lepley is Senior Computer Vision Engineer at NVIDIA and the NVIDIA representative in the Khronos OpenVX standardization group. His focus is on the development of optimized computer vision toolkits and libraries for real-time embedded systems. Earlier, Thierry was Principal Engineer at STMicroelectronics, working on many-core acceleration for computer vision, where he developed a compiler that automates the parallelization of image processing pipelines and the management of image tiling.
OpenVX Tutorial Material and Slides
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