Tony Jebara - From Perception and Discriminative Learning to Interactive Behavior

http://www.researchchannel.org/prog/displayevent.aspx?rID=2748&fID=345

A strong symbiosis lies between machine learning and machine perception. Just as we learn to reason and interact with the world through our senses, a smart sensing system could acquire data to drive higher level learning problems. Ironically, learning and probabilistic methods themselves can provide the driving machinery for perception as well. I demonstrate several examples of probabilistic sensors in wearable and room-based environments. These human-centered systems perform object detection, face tracking, 3d modeling, recognition, and topic-spotting in real-time.