Finding efficient algorithms to describe, measure and compare shapes is a
central problem in
numerous disciplines that generate extensive quantitative and visual
Among these, biology occupies a central place. Registration of brain anatomy
is essential to many studies in neurobiology; at a molecular level, comparison
of protein shapes is
a key step in understanding the relationships between their functions.
In this talk I will introduce the idea of a globally optimal conformal mapping
(discrete) surfaces of genus zero as one method to solve this problem.
In this approach, the whole mesh representing the source surface is warped onto
the target surface,
using the mapping defined through the composition of discrete conformal
mappings of the surfaces
onto the sphere and the Mobius transformation between these mappings.
The Mobius transformation is then optimized to lead to minimal distortion
between the source mesh
and its image, where distortion is measured as difference from isometry.
I will show that this approach leads to the definition of a metric in the space
surfaces. I will describe the implementation of this approach into a software
and its applications
on biological examples, from brain surface matching to 3D morphometrics on
bones of primates.
I was born and grew up in France. After graduation from the Ecole Centrale de
a higher education establishment for engineers, I completed a PhD program in
and biophysics at the University Louis Pasteur of Strasbourg, France. The same
year, I was appointed
staff scientist of the CNRS, and joined the biological NMR laboratory at the
University Louis Pasteur
of Strasbourg, France. In 1997, I came for a sabbatical to Stanford University.
I liked California
so much that I decided to stay: in 2004, I joined the University of California,
Davis, with a
joint appointment as Professor in the department of Computer Science and member
of its Genome Center.
Dr. Yiying Tong