Using Computer Vision and Machine Learning with the Open Source Computer Vision Library

Location: Marina Ballroom F Level: Intermediate

This talk will present a rapid tutorial on how to use computer vision and machine learning in applications using the Open Source Computer Vision Library.

We will give a meta-view of computer vision and machine learning, give demonstrations of algorithms used in security, image retrieval and robotics, and provide a tutorial sufficient to get you up and running immediately.

The talk will end with a projection of where computer vision is going on the Web and in robotics.

Photo of Gary Bradski

Gary Bradski

Dr. Gary Rost Bradski is a consulting professor in the CS department at Stanford University AI Lab where he mentors robotics, machine learning and computer vision research. He is also Senior Scientist at Willow Garage. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. His current interest is perception based dexterous grasping and manipulation for robots. Towards this goal, some open source tools Gary started are the Open Source Computer Vision Library (OpenCV ), the statistical Machine Learning Library (MLL comes with OpenCV), and the Probabilistic Network Library (PNL). OpenCV is used around the world in research, government and commercially (for example in wide use within Google). All libraries are open, and free on Source Forge for commercial or research purposes. The vision libraries use and helped develop a notable part of the commercial Intel performance primitives library (IPP Gary led the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize and helped found the Stanford AI Robotics project at Stanford Gary has over 50 publications and 13 issued patents with 18 pending. He lives in Palo Alto with his wife and 3 daughters and bikes road or mountains as much as he can.


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