General Information
Modern society increasingly relies on the collection, processing, and simulation of massive data volumes. While we have been extremely successful in developing these techniques, the current trend towards more realistic physics-based simulations and inferences reveal shortcomings in our ability to handle high-dimensional data volumes. Examples of areas that are struggling with this data deluge include computer graphics and gaming, with consumers demanding more and more realism; as well as seismic and medical imaging, where there is incessant push towards higher resolution images and better inferences on what these images contain. The Image and Data Processing Group aims to develop new techniques to address this challenge.
Reconstructing detail
At UBC, we have a very strong and diverse group of faculty working on various aspects of image and data processing. We have faculty in the fields PDE- and differential-geometry-based image generation and registration, and we also have faculty active in the fields of computational and applied harmonic analysis (wavelets); compressive sensing (a new paradigm in sampling); convex optimisation; machine learning; and PDE-constrained optimisation.
Core Faculty
| Uri Ascher | Uri is a Professor of Computer Science and a former Director of the IAM (1993-98). The focus of his work is the investigation and promotion of novel, efficient and reliable methods in scientific computation, particularly for approximation problems involving differential equations with constraints. Examples of Uri’s specific research areas are robotics, virtual reality, data inversion in geophysics, multibody systems simulation, 3D electromagnetic modelling, image reconstruction, and 3D mesh denoising. |
| Robert Bridson | Robert is a Professor of Computer Science and a member of the Imager lab. His work revolves around numerical and geometric algorithms for solving problems in fluid and solid mechanics, including iterative methods for linear systems, discretisation of PDEs in space and time, mesh generation, and collision and contact handling. He often works closely with film studios in applying these algorithms to problems in physics-based animation. |
| Michael Friedlander | Michael is a Professor of Computer Science. His research is primarily in developing numerical methods for large-scale optimisation. He is especially interested in issues of convergence analysis, robust software implementation, and applications in signal processing and image reconstruction. His recent contributions include software packages for large-scale sparse optimisation (SPGL1), sparse signal reconstruction (Sparco), and a linear operator toolbox (SPOT). |
| Eldad Haber | Eldad is a Professor of Mathematics and of Earth and Ocean Sciences. His main field of interest is the development of computational methods for inverse problems with applications to geophysical and medical imaging. The field is interdisciplinary by nature and includes numerical discretisation of partial differential equations, numerical optimisation and robust statistics. Eldad is an NSERC Industrial Research Chair in Computational Geoscience. |
| Özgür Yilmaz | Özgür is a Professor in the Department of Mathematics. His main research areas are quantisation of redundant expansions, blind source separation, and sparse approximations. He is a co-author of Sparco, a toolbox for testing sparse reconstruction toolbox algorithms. |
| Alla Sheffer | Alla is a Professor of Computer Science and a member of the Imager lab. She conducts research in digital shape modeling and geometry processing, with applications to computer graphics and computer-aided engineering. Her work utilises tools from computational and differential geometry, discrete mathematics, and graph theory to generate, manipulate, and edit discrete geometric models. |
Recommended Courses
Students interested in Image and Data Processing research in the IAM are advised to take the following preliminary, specific, and optional courses:
Preliminary and Foundational Courses
CPSC 302: Numerical Computation for Algebraic Problems
CPSC 303: Numerical Approximation and Discretization
CPSC 314: Computer Graphics
MATH 340: Introduction to Linear Programming
MATH 405: Numerical Methods for Differential Equations
CPSC 542G: Introduction to Numerical Methods
Specific Courses
EOSC 513: Imaging and Estimation with Wavelets
CPSC 524: Computer Graphics: Modeling
CPSC 526: Computer Animation
CPSC 530P: Sensorimotor Computation
MATH 555: Compressed Sensing
Further Options
CPSC 533D: Animation Physics
EOSC 550: Linear Inverse Theory
EOSC 555: Nonlinear Inverse Theory
CPSC 564: Data Mining