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Computer Vision Distinguished Speaker Series
Object Recognition from Invariant Local Features
Dr. David Lowe
Monday, February 19, 2007
1:30PM Pegasus Ballroom, Student Union
Abstract
Human vision is so powerful that we seldom give a second thought to our ability to immediately identify the objects in our surroundings. However, the problem of object recognition has proven to be very challenging for computer vision. A major source of the difficulty is the large range of variations in appearance that may occur, due to factors such as changes in 3D viewpoint, varying illumination, partial visibility, and background clutter. Fortunately, there has been rapid progress on this problem within the past few years through the use of invariant local feature matching. Thousands of these local features can be extracted from an image to describe small overlapping regions, and each feature is designed to be invariant to a range of image transformations, such as changes in scale, orientation, brightness, and local deformations. This talk will present an overview of the invariant feature approach, as well as some recent applications such as location recognition and automated stitching of digital images into panoramas.
Short Bio
David Lowe is a professor of Computer Science at the University of British Columbia and a Fellow of the Canadian Institute for Advanced Research. He received his Ph.D. in computer science from Stanford University in 1984. From 1984 to 1987 he was an Assistant Professor at the Courant Institute of Mathematical Sciences at New York University. He is a member of the scientific advisory board for Evolution Robotics. His research interests include object recognition, local invariant features for image matching, robot localization, and computational models of human visual recognition.
Dr. David Lowe
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