Riemannian manifolds, kernels and learning


July 2, 2013


Richard Hartley


Australian National University


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


Richard Hartley

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 also co-leader of the Computer Vision group in NICTA, a research laboratory 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. He worked on several Imaging projects, including medical imaging, document imaging and visual inspection. In 1991, he began an extended research effort in the area of applying projective geometry techniques to reconstruction. 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 decade’s research in this area.
University of Toronto, Canada PhD Mathematics, 1976, MSc 1972 ; Stanford University, MSc Computer Science, 1985 ; Australian National University, BSc, 1971