Next: Improved Fast Gauss Transform
Research Summary
Changjiang Yang
Computer Vision Laboratory, Department of Computer Science
University of Maryland, College Park, MD 20742, USA
My research has spanned three areas that are closely related to each
other: computer vision, machine learning and scientific computing.
The goal of computer vision is to build a machine which can see the
world as human beings. To achieve this goal, some form of human
intelligence is indispensable, just as people see the outside world
by understanding it first. On the other hand, computer vision is a
very important research area for the machine learning, and it has a
large amount of applications. Usually the real-world problems in
vision and learning are complicated, high-dimensional, non-linear
and large-scale. To solve these problems, efficient algorithms are
required. Therefore scientific computing has played an important
role in vision and learning, and it will continuously be more
important.
My research contributions can be divided into the following closely
related topics.
- Fast Gauss transform has been studied and we find that it is
inefficient in higher dimensional spaces. We developed a new
improved fast Gauss transform using algorithms from approximation
algorithms and numerical analysis. The improved fast Gauss transform
has been applied to various applications, such as efficient kernel
density estimation, image segmentation, image registration, object
tracking, etc.
- Many methods in vision or learning require the evaluation of the
similarity measures between two probability distributions. The
commonly used similarity measures such as Kullback-Leibler distance
and Bhattacharyya distance are limited to one or two feature
dimensions, due to the difficulty in estimating the entropy of the
high-dimensional features. We proposed a similarity measure which is
the sum of all pair-wise kernelized distances between two
distributions. It can be efficiently computed using our improved
fast Gauss transform.
- Visual tracking is an important and active research area
in computer vision. We used our similarity measure and mean-shift technique
to track single object in a joint feature-spatial space, and
achieved more accurate and robust tracking performance. For
multiple object tracking, we used distinctive features and
particle filter framework to simultaneously and reliably track
them. The quasi-random sampling and efficient distinctive
features are used to achieve a realtime tracking speed.
We have submitted and published these work in computer science and
applied mathematics journals and conferences. It is also available
as technical reports in Department of Computer Science, University
of Maryland at College Park.
Next: Improved Fast Gauss Transform
Changjiang Yang
2005-03-01