[4eyes] Talk on Feb. 16 - Model-Based Perceptual Grouping and Shape Abstraction

[Matthew Turk]

Reminder of tomorrow's (holiday) talk by Sven Dickinson, chair of the CS department at Toronto:

-----Original Message-----
From: Matthew Turk [mailto:mturk at cs.ucsb.edu] 
Sent: Tuesday, February 03, 2015 10:37 AM
To: faculty at cs.ucsb.edu; grads at cs.ucsb.edu; ilab-users at lists.cs.ucsb.edu; manj at ece.ucsb.edu; Tim Cheng; Miguel Eckstein; Michael Liebling
Subject: Talk on Feb. 16 - Model-Based Perceptual Grouping and Shape Abstraction

Although Monday, Feb. 16 is a holiday, I have a visitor from the University 
of Toronto who will give a talk that day, and all are invited:

Time and location: 2:00pm, CS Conference Room (1132 HFH)
Title: Model-Based Perceptual Grouping and Shape Abstraction

Sven Dickinson
Department of Computer Science
University of Toronto
http://www.cs.toronto.edu/~sven/

Abstract:

For many object classes, shape is the most generic feature for object 
categorization. However, when a strong shape prior, i.e., a target
object, is not available, domain independent, mid-level shape priors must 
play a critical role in not only grouping causally related
features, but regularizing or abstracting them to yield higher-order shape 
features that support object categorization. In this talk, I will
present a framework in which mid-level shape priors take the form of a 
vocabulary of simple, user-defined 2-D part models.  From the
vocabulary, we learn to not only group oversegmented regions into parts, but 
to abstract the shapes of the region groups, yielding a set of
abstract part hypotheses.  However, the process of shape abstraction can be 
thought of as a form of "controlled hallucination", which comes at
the cost of many competing 2-D part hypotheses.  To improve part hypothesis 
precision, we present two approaches that exploit the context
of the hypotheses.  In the first approach, we exploit spatiotemporal 
coherence (temporal context) of part hypotheses in a dynamic
environment, and formulate hypothesis selection in a graph-theoretic, 
probabilistic framework. In the second approach, we assume that the 2-D 
parts represent the component faces of aspects that model a vocabulary of 
3-D part models. We then exploit the relational structure (spatial
context) of the faces encoded in the aspects, and again formulate hypothesis 
selection in a graph-theoretic, probabilistic framework.
Finally, we introduce a technique that is able to recover the pose and shape 
of a volumetric part from a recovered aspect, yielding a framework
that revisits the classical problem of recovering a set of qualitative 3-D 
volumetric parts from a single 2-D image.