Many tasks in imaging, computer graphics and computer vision can be formulated as a mapping problem. The goal is to look for a suitable mapping between two corresponding data. Some examples include finding surface parameterization for texture mapping in computer graphics, warping map for image registration and shape matching for shape analysis. It calls for effective algorithms to compute meaningful mappings with desirable constraints. Recently, there are tremendous developments in the area of mapping problems. This mini-symposium aims at enhancing interaction of scholars working in this field.

A fast solver for locally rigid image registration

James Herring (Emory University)

A new constrained image registration model to avoid folding

Jin Zhang (Liao-Cheng University)

Medical image analysis based on artificial intelligence and its clinical application

Dexing Kong (Zhejiang University)

Longitudinal MRI brain analysis on image manifold

Shi-hui Ying (Shanghai University)

Non-isometric shape matching via conformal Laplace-Beltrami Basis Pursuit

Stephan Schonsheck (Rensselaer Polytechnic Institute (RPI))

On a new multigrid algorithm for image segmentation

Mike Roberts (University of Liverpool)

Parametrising flat-foldable surfaces with incomplete data

Di Qiu (The Chinese University of Hong Kong)

Surface mapping using Teichmuller theory

Xianfeng Gu (State University of New York at Stony Brook )

Variational Diffeomorphic Models for Image Registration

Daoping Zhang (University of Liverpool)

Fuzzy based energy model for Segmentation of images using hybrid image data

Noor Badshah (University of Engineering and Technology Peshawar)

Topology Preserving Image Segmentation by Beltrami Signature of Images

Hei Long Chan (The Chinese University of Hong Kong)

Sobolev Gradient and Segmentation of Vector Valued Texture Images

Fahim Ullah (University of Engineering and Technology Peshawar)

Organizers:

Ke Chen (University of Liverpool)

Ronald Lui (Chinese University of Hong Kong)

Keywords:

computer graphics, computer vision, image registration, machine learning, nonlinear optimization, partial differential equation models