MIT talk: Object and Place Recognition from Invariant Local Features

speaker: David Lowe , University of British Columbia
date: 2005/12/12

abstract:
Within the past few years, invariant local features have been successfully applied to a wide range of recognition and image matching problems. For recognition applications, it has proved particularly important to develop features that are distinctive as well as invariant, so that a single feature can be used to index into a large database of features from previous images. Robust recognition can then be achieved by identifying clusters of features with geometric consistency followed by detailed model fitting. Efficiency can be obtained with approximate nearest-neighbor methods that identify matches in a large database in real time. Recent work will be presented on applications to location recognition, augmented reality, and the detection of image panoramas from unordered sets of images.

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 models of human visual recognition.