MPhil Thesis Defence


"An Enhanced Tensor Voting Framework and Applications"

By

Mr. Dickson Wai-Shun Tong


Abstract

Recently, a computational framework for feature extraction and
segmentation, Tensor Voting, has been proposed. It has demonstrated its
efficacy in various vision problems with encouraging results. It is a
unified framework, which makes use of the continuity constraint to infer
the geometrical features and generate layered description in terms of
surfaces, regions, curves, and junctions. The formalism not only is
capable of dealing with multiple structures, detecting and localizing
discontinuities, it is also very robust against outlier noise. No
iterative optimization search is required.

Further research on the formalism leads to a number of improvements that
are originally absent in the basic formalism. This thesis consists of
theory and application aspects on tensor voting. First, we enhance the
basic tensor voting theory with first order information. Inference of
important geometrical features, such as region and surface boundaries, and
curve endpoints can be achieved. These enhancements enable us to derive an
adaptive scale detection algorithm.

With the improved formalism, two applications, one in computer graphics
and the other in medical surface extraction, are proposed. A novel and
effective algorithm on epipolar geometry estimation, which takes the
benefit of the latest advancement of the higher dimensional tensor voting
formalism is also derived.


Date:			Thursday, 23 August 2001

Time:			3:00p.m.-5:00p.m.

Venue:			Room 2302
			Lifts 17-18

Committee Members:		Dr. Chi-Keung Tang (Supervisor)
			Dr. Long Quan (Chairman)
			Dr. Tien-Tsin Wong (CUHK)


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