Csls workshop on computational vision and image analysis

Workshop overview

Great advances have been made in the acquisition of image data, from conventional photography, CT scanning, and satellite imaging to thenow ubiquitous digital cameras embedded in cell phones and other wireless devices. Although the semantic understanding of the shapesand other objects appearing in images is effortless for human beings, the corresponding problem in machine perception - namely, automaticinterpretation via computer programs - remains a major open challenge in modern science. In fact, there are very few systems whose valuederives from the analysis rather than collection of image data, and this "semantic gap" impedes scientific and technological advances inmany areas, including automated medical diagnosis, robotics, industrial automation, and effective security and surveillance.In this CSLS Workshop, three distinguished experts in the field of Computational Vision and Image Analysis share their thoughts on thecurrent state of the art and future directions in the field.

• Go to the talk on Hierarchical Designs for Pattern Recognition (by Prof. Donald Geman)
• Go to the talk on Modeling and Inference of Dynamic Visual Processes (by Prof. Stefano Soatto)
• Go to the talk on Computational Anatomy and Models for Image Analysis (by Prof. Michael Miller)

Remark: This workshop was held on October 30, 2003 as part of the Computational Sciences Lecture Series (CSLS) at the University of Wisconsin-Madison.

Hierarchical designs for pattern recognition

By Prof. Donald Geman (Dept. of Applied Mathematics and Statistics and Center for Imaging Science,Johns Hopkins University, USA)

Slides of talk [PDF] (Not yet available.) | Video [WMV] | Video [MPG]

ABSTRACT: It is unlikely that complex problems in machine perception, such as scene interpretation, will yield directly to improved methodsof statistical learning. Some organizational framework is needed to confront the small amount of data relative to the large number ofpossible explanations, and to make sure that intensive computation is restricted to genuinely ambiguous regions. As an example, I willpresent a "twenty questions" approach to pattern recognition. The object of analysis is the computational process itself rather thanprobability distributions (Bayesian inference) or decision boundaries (statistical learning). Under mild assumptions, optimal strategiesexhibit a steady progression from broad scope coupled with low power to high power coupled with dedication to specificexplanations. 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).

Modeling and inference of dynamic visual processes

By Prof. Stefano Soatto (Department of Computer Science, University of California Los Angeles,USA)

Slides of talk [PDF] (Not yet available.) | Video [WMV]

ABSTRACT: "We see in order to move, and we move in order to see." Inthis expository talk, I will explore the role of vision as a sensor for interaction with physical space. Since the complexity of thephysical world is far superior to that of its measured images, inferring a generic representation of the scene is an intrinsicallyill-posed problem. However, the task becomes well-posed within the context of a specific control task. I will display recent results inthe inference of dynamical models of visual scenes for the purpose of motion control, shape visualization, rendering, and classification.

Computational anatomy and models for image analysis

By Prof. Michael Miller (Director of the Center for Imaging Science, The Seder Professor of Biomedical Engineering,Professor of Electrical and Computer Engineering, Johns Hopkins University,USA)

Slides of talk [PDF] (Not yet available.) | Video [WMV]

ABSTRACT: University Recent years have seen rapid advances in the mathematical specification of models for image analysis of humananatomy. 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 dimensionaldiffeomorphisms. 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, andMatching: A General Framework" (Miller and Younes, Int. J. Comp. Vision Vol. 41, 61-84, 2001), "On the Metrics ofEuler-Lagrange Equations of Computational Anatomy (Annu. Rev. Biomed. Eng., Vol. 4, 375-405, 2002). Numerous resultswill be shown comparing shapes through this metric formulation of the deformable template, including results from disease testing on thehippocampus, and cortical structural and functional mapping.