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<p class=MsoNormal>I’ll send complete information later, but for now, in
case you want to reserve this on your schedule:<o:p></o:p></p>
<p class=MsoNormal><b><o:p> </o:p></b></p>
<p class=MsoNormal><b>Visual recognition and reconstruction in the
three-dimensional world<o:p></o:p></b></p>
<p class=MsoNormal>Silvio Savarese<o:p></o:p></p>
<p class=MsoNormal>University of Michigan<o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'><a
href="http://www.eecs.umich.edu/~silvio/index.html">http://www.eecs.umich.edu/~silvio/index.html</a><o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'><o:p> </o:p></p>
<p class=MsoNormal style='text-align:justify'>Date: February 2<o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'>Time: 3:30pm (probably)<o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'>Location: TBD<o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'><o:p> </o:p></p>
<p class=MsoNormal style='text-align:justify'>The ability to interpret the
semantic of objects and actions, their individual geometric attributes as well
as their spatial and temporal relationships within the environment is essential
for an intelligent visual system and extremely valuable in numerous applications.
In visual recognition, the problem of categorizing generic objects is a highly
challenging one. Single objects vary in appearances and shapes under various
photometric (e.g. illumination) and geometric (e.g. scale, view point,
occlusion, etc.) transformations. Largely due to the difficulty of this
problem, most of the current research in object categorization has focused on
modeling object classes in single (or nearly single) views. But our world is
fundamentally 3D and it is crucial that we design models and algorithms that
can handle such appearance and pose variability. In the first part of the talk
I introduce a novel framework for learning and recognizing 3D object categories
and their poses. Our approach is to capture a compact model of an object
category by linking together diagnostic parts of the objects from different
viewing points. The resulting model is a summarization of both the appearance
and geometry information of the object class. Unlike earlier attempts for 3D
object categorization, our framework requires minimal supervision and has the
ability to synthesize unseen views of an object category. Our results on
categorization show superior performances to state-of-the-art algorithms on the
largest dataset up to date. In the second part, I present a new framework for
modeling the overall geometrical and temporal organization of scenes. This is
done by learning the typical distribution of spatial and temporal relationships
among elements in scenes. Our model is extremely compact and can be learned in
an unsupervised fashion. Experiments demonstrate that the added ability of
modeling such spatial and temporal relationships is useful in several
recognition tasks, such as scene/object categorization and human action
classification. I will conclude the talk with final remarks on the relevance of
the proposed research for a number of applications in mobile vision.<o:p></o:p></p>
<p class=MsoNormal style='text-align:justify'><o:p> </o:p></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><o:p> </o:p></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><b>Bio:<o:p></o:p></b></p>
<p class=MsoNormal style='text-align:justify'><b>Silvio Savarese</b> is an
Assistant Professor of Electrical Engineering at the University of Michigan,
Ann Arbor. He earned his PhD in Electrical Engineering from the California
Institute of Technology in 2005. He joined the University of Illinois at
Urbana-Champaign from 2005 to 2008 as a Beckman Institute Fellow. In 2002 he
was a recipient of the Walker von Brimer Award for outstanding research
initiative. His research interests include computer vision, object and scene
recognition, shape representation and reconstruction, human visual perception
and visual psychophysics.<o:p></o:p></p>
<p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p>
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