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Ahmed Elgammal
Department of Computer Science, Rutgers University
Friday, Apr 8., 11:00am
LC 102, Brooklyn Campus, Polytechnic University
Abstract
Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support
tasks such as synthesis, pose recovery, reconstruction, and tracking. In this talk we introduce a framework for
learning generative models for dynamic appearance. We use nonlinear dimensionality reduction to achieve an embedding
of the global deformation manifold that preserves the geometric structure of the manifold. Given such embedding, a
nonlinear mapping is learned from such embedded space into the visual input space with a closed-form solution for the
inverse mapping which facilitates recovery of the intrinsic body configuration and therefore pose recovery. We also
address the question of separating style and content on manifolds representing dynamic objects. We learn decomposable
generative models that explicitly decompose the intrinsic body configuration (content) as a function of time from the
appearance (style) of the person performing the action as time-invariant parameter. We show results on gait data as well as
facial expression data.
Bio
Dr. Ahmed Elgammal is an assistant professor at the Department of Computer Science, Rutgers, the State University of
New Jersey Since Fall 2002. Dr. Elgammal is also a member of the Center for Computational Biomedicine Imaging and
Modeling (CBIM) at Rutgers. His primary research interest is computer vision and machine learning. His research focus
includes human activity recognition, human motion analysis, tracking, human identification, and statistical methods
for computer vision. He develops robust real-time algorithms to solve computer vision problems in areas such as visual
surveillance, visual human-computer interaction, virtual reality, and multimedia applications. Dr. Elgammal interest
includes also research on document image analysis.
Dr. Elgammal received his B.Sc. and M.Sc. degrees in computer science and automatic control from University of
Alexandria, Egypt in 1993 and 1996, respectively. He received another M.Sc. and his Ph.D. degree in computer science
from the University of Maryland, College Park, in 2000 and 2002 respectively.
For more information please contact Joshua Gluckman (jgluckma at duke.poly.edu)