Learning to Find and Match Feature Points

Speaker:        Professor Pascal Fua
                School of Computer and Communication Science
                EPFL

Title:          "Learning to Find and Match Feature Points"

Date:           Monday, 10 December 2018

Time:           11:00am - 12 noon

Venue:          Room 5506 (via lift 25/26), HKUST

Abstract:

Finding and matching feature points is at the heart of many Computer
Vision algorithms ranging from stitching panoramas to searching image
databases and from automated 3D reconstruction to augmented reality.

In this talk, I will present Deep Network architectures that implement
the full feature point-handling pipeline, that is, detection, orientation
estimation, feature description, and matching. While previous works have
successfully tackled each one of these problems individually, our approach
involves learning to do all four in a unified manner while preserving
end-to-end differentiability. The resulting pipeline outperforms
state-of-the-art methods on a number of benchmark datasets, without having
to retrain.


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Biography:

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris,
in 1984 and the Ph.D. degree in Computer Science from the University of
Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in
1996 where he is a Professor in the School of Computer and Communication
Science. Before that, he worked at SRI International and at INRIA
Sophia-Antipolis as a Computer Scientist. His research interests include
shape modeling and motion recovery from images, analysis of microscopy
images, and Augmented Reality. He has (co)authored over 300 publications
in refereed journals and conferences. He is an IEEE Fellow and has been an
Associate Editor of IEEE journal Transactions for Pattern Analysis and
Machine Intelligence. He often serves as program committee member, area
chair, and program chair of major vision conferences and has cofounded two
spinoff companies.