PhD in Computer Science & Engineering
Thursday, June 30, 2011
9:30 a.m. – 11:00 a.m.
Swearingen Room 2A31
ABSTRACT
3D Shape Correspondence:
Beyond Groupwise Methods and Smooth, Closed Surfaces
For identifying the disease-effected regions using medical images, it is
important to bring all individual instances into the common space for
comparison (between normal and abnormal groups). To achieve this,
one way is to identify the shape-based correspondences between
multiple instances of the same organ. The objective of shape
correspondence then is to identify corresponding landmarks across a
population of instances of the same shape. However, the non-rigid
variation observed in naturally occurring 3D structures leads to a highly
non-linear and complex problem. While various efforts have been made
to address the problem of shape correspondence in previous research
works, most existing methods are limited in their application to the
smooth, closed-surface type of shape. Due to the importance of shape
correspondence in various computer vision tasks, especially medical
imaging-related applications, this work investigates the problem of
shape correspondence for the case of open-surface shapes and highly
convoluted surfaces. This work introduces a novel 3D landmark sliding
framework which can be used to bring both closed-surface and open-
surface shapes into correspondence. Further, this work introduces the
concept of topological consistency of landmarks, which has previously
not been included in shape correspondence research. The developed
landmark sliding framework and topology consistency are able to
perform shape correspondence more accurately and efficiently
compared to existing methods. Finally, this work introduces a method
of organizing a population of shape instances into a novel tree
structure to minimize the shape correspondence errors by pairing
similar instances together while constraining the height of the tree to
minimize accumulation of errors.
