Workshop on Computational Vision and Image Analysis
Thursday, 30 October
2:30 PM - 3:30 PM
Hierarchical Designs for Pattern Recognition
Dept. of Applied Mathematics and Statistics and
Center for Imaging Science
Johns Hopkins University
ABSTRACT: It is unlikely that complex problems in
machine perception, such as scene interpretation, will yield directly
to improved methods of statistical learning. Some organizational
framework is needed to confront the small amount of data relative
to the large number of possible explanations, and to make sure that
intensive computation is restricted to genuinely ambiguous regions.
As an example, I will present a "twenty questions" approach to pattern
recognition. The object of analysis is the computational process
itself rather than probability distributions (Bayesian inference)
or decision boundaries (statistical learning). Under mild assumptions,
optimal strategies exhibit a steady progression from broad scope
coupled with low power to high power coupled with dedication to
specific explanations. Several theoretical results will be mentioned
(joint work with Gilles Blanchard) as well as experiments in object
detection (joint work with Yali Amit and Francois Fleuret).
3:45 PM - 4:45 PM
Modeling and Inference of Dynamic Visual Processes
Department of Computer Science
University of California Los Angeles
ABSTRACT: "We see in order to move, and we move in order to see."
In this expository talk, I will explore the role of vision as a
sensor for interaction with physical space. Since the complexity
of the physical world is far superior to that of its measured images,
inferring a generic representation of the scene is an intrinsically
ill-posed problem. However, the task becomes well-posed within the
context of a specific control task. I will display recent results
in the inference of dynamical models of visual scenes for the purpose
of motion control, shape visualization, rendering, and classification.
5:00 PM - 6:00 PM
Computational Anatomy and Models for Image Analysis
Director of the Center for Imaging Science
The Seder Professor of Biomedical Engineering
Professor of Electrical and Computer Engineering
ABSTRACT: University Recent years have seen rapid advances in the
mathematical specification of models for image analysis of human
anatomy. As first described in "Computational Anatomy: An Emerging
Discipline" (Grenander and Miller, Quarterly of Applied Mathematics,
Vol. 56, 617-694, 1998), human anatomy is modelled as a deformable
template, an orbit under the group action of infinite dimensional
diffeomorphisms. In this talk, we will describe recent advances
in CA, specifying a metric on the ensemble of images, and examine
distances between elements of the orbits, "Group Actions, Homeomorphisms,
and Matching: A General Framework" (Miller and Younes, Int. J. Comp.
Vision Vol. 41, 61-84, 2001), "On the Metrics of Euler-L agrange
Equations of Computational Anatomy (Annu. Rev. Biomed. Eng., Vol.
4, 375-405, 2002). Numerous results will be shown comparing shapes
through this metric formulation of the deformable template, including
results from disease testing on the hippocampus, and cortical structural
and functional mapping.
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