April 16, 2014


Inverse Problems and Image Analysis Seminar
July 2013 - June 2014

Hosted by the Fields Institute

Organizers: Abdol-Reza Mansouri (Queen's U)
Adrian Nachman (U of Toronto)

Upcoming Seminars

Past Seminars

Thursday Jan. 23
1:30 p.m.

Room 230


Martin Bauer, University of Vienna
Riemannian Geometry of Shape Spaces

I will provide an overview of various notions of shape spaces, including the space of parametrized and unparametrized surfaces. I will discuss the Riemannian metrics that can be defined thereon, and what is known about the properties of these metrics. I will put particular emphasis on the induced geodesic distance, the geodesic equation and its well-posedness, geodesic and metric completeness and properties of the curvature. In addition I will present selected numerical examples illustrating the behavior of these metrics.

Nov. 14, 2013
2:10 pm
** in Huron 1018**

Klas Modin, University of Toronto and Chalmers University of Technology, Göteborg, Sweden
Diffeomorphic Image Registration

In this informal talk, I will present the framework of "large deformation diffeomorphic metric matching". This framework is used for non-rigid registration of grey-scale images. I will focus on the underlying Riemannian geometry and the connection to fluid mechanics through Euler-Arnold equations.

Thursday July 18
2:00 p.m.

Stewart Library


Sung Ha Kang, Georgia Institute of Technoogy
Variational and RKHS Approach for Image Colorization and Segmentation

This talk will start with an introduction to image restoration, starting from Total Variation minimizing denoising, and consider inpainting and colorization problems. The term ``colorization'' was introduced by Wilson Markle who first processed the gray scale moon image from the Apollo missions. A couple of variational colorization models will be presented which demonstrate different effects. Another appeoach that uses a Reproducing Kernel Hilbert Space method will be presented for an effective colorization application. A link to image segmentation will be made through a medical image application. Image segmentation separates the image into different regions to simplify the image and identify the objects easily. The Mumford-Shah and Chan-Vese models are some of the most well-known variational models in the field. If time permits, this talk will include a model segmenting piecewise constant images with irregular object boundaries, and consider some features of multiphase segmentation.







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