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First Meeting:
Workshop on Computational Vision and Image Analysis

Thursday, 30 October 2003


2:30 PM - 3:30 PM
Hierarchical Designs for Pattern Recognition

Donald Geman

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).

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3:45 PM - 4:45 PM
Modeling and Inference of Dynamic Visual Processes

Stefano Soatto

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.
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5:00 PM - 6:00 PM
Computational Anatomy and Models for Image Analysis

Professor Michael Miller

Director of the Center for Imaging Science
The Seder Professor of Biomedical Engineering
Professor of Electrical and Computer Engineering
Johns Hopkins

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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