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Energy Aware Visual Recognition

May 23, 2012

Using local invariant features has been proven by published literature to be a powerful tool for image processing and pattern recognition tasks. However, in energy constraint environments, these invariant features would not scale easily because of their computational requirements. Motivated to find efficient building recognition algorithms based on the scale invariant feature transform (SIFT) keypoints, we discuss in this talk results of uSee, a supervised learning framework that identifies subsets of relevant keypoints. With only 14.3% of an image SIFT keypoints, uSee exceeded prior literature and identified with an accuracy of 99.1% buildings in the Zurich Building Database (ZuBud).

Aside from the energy aware building recognition project, Dr. Awad will be also presenting some of her research work on biologically inspired deep belief networks, wild sport video analysis and interactive environments using mobile apps.

Dr. Mariette Awad

Electrical and Computer Engineering Department
American University of Beirut

Download the presentation slides

Event Location

TU Vienna
Zemanek Lecture Room (Room Number: HHEG01)<br />Favoritenstraße 9-11, Stiege III, ground floor, light green area
1040 Vienna
Austria