Speaker: Richard Hartley, Australian National University
Title: Riemannian Manifolds, Kernels and Learning
I will talk about recent results from a number of people in the group on Riemannian manifolds in computer vision. In many Vision problems Riemannian manifolds come up as a natural model. Data related to a problem can be naturally represented as a point on a Riemannian manifold. This talk will give an intuitive introduction to Riemannian manifolds, and show how they can be applied in many situations. Examples that will be considered are the Essential manifold, relevant in structure from motion; the manifold of Positive Definite matrices and the Grassman Manifolds, which have a role in object recognition and classification, and the Kendall shape manifold, which represents the shape of 2D objects.
Professor Richard Hartley is head of the computer vision group in the Department of Information Engineering, at the Australian National University, where he has been since January, 2001. He is also the Program Leader for the Autonomous Systems and Sensor Technology Program of National ICT Australia, a research centre set up in 2002 with funding from the Australian Government. Dr. Hartley worked at the General Electric Research and Development Center from 1985 to 2001. During the period 1985-1988, he was involved in the design and implementation of Computer-Aided Design tools for electronic design and created a very successful design system called the Parsifal Silicon Compiler. In 1991 he was awarded GE's Dushman Award for this work. He became involved with Image Understanding and Scene Reconstruction working with GE's Simulation and Control Systems Division. This division built large-scale flight-simulators. Dr. Hartley's projects in this area were in the construction of terrain models and texture mosaics from aerial and satellite imagery. This involved research in camera modelling, stereo matching and scene reconstruction. In 1991, he began an extended research effort in the area of applying projective geometry techniques to reconstruction using calibrated and semi-calibrated cameras. This research direction was one of the dominant themes in computer vision research throughout the 1990s. In 2000, he co-authored (with Andrew Zisserman) a book for Cambridge University Press, summarizing the previous decadea s research in this area. >From 1995 he was GE project leader for a shared-vision project with Lockheed-Martin involving design and implementation of algorithms for an AFIS (fingerprint analysis) system being developed under a Lockheed-Martin contract with the FBI. This involved work in feature extraction, interactive fingerprint editing and fingerprint database matching. He also investigated application of fingerprint scanners to point of sale systems. Under this contract he also led work on applications of DNA database technology.
For additional information contact: Kyros Kutulakos