IEEE Transactions on Pattern Analysis and Machine Intelligence

       Call for Papers

Special Issue on Probabilistic Graphical Models in Computer Vision

Guest Editors: Qiang Ji, Rensselaer Polytechnic Institute; Jiebo Luo, Kodak 
Research; Dimitris Metaxas, Rutgers University; Antonio Torralba, Massachusetts 
Institute of Technology; Thomas Huang, University of Illinois at 
Urbana-Champaign, and Erik Sudderth, University of California at Berkeley.


Topic Description and Justification
An exciting development over the last decade has been the gradually widespread 
adoption of probabilistic graphical models (PGMs) in many areas of computer 
vision and pattern recognition. Many problems in computer vision can be viewed 
as the search, in a specific domain, for a coherent global interpretation and 
understanding from local, uncertain, and ambiguous observations.  Graphical 
models provide a unified framework for representing the observations and the 
domain-specific contextual knowledge, and for performing recognition and 
classification through rigorous probabilistic inference.  In addition, PGMs 
readily capture the correlations and dependencies among the observations, as 
well as between observations and domain or commonsense knowledge, and allow 
systematic quantification and propagation of the uncertainties associated with 
data and inference.

Graphical models can be classified into directed and undirected models. The 
directed graphs include Bayesian Networks (BNs) and Hidden Markov Models 
(HMMs), while the undirected graphs include Markov Random Fields (MRFs) and 
Conditional Random Fields (CRFs).  Both directed and undirected graphical 
models have been widely used in computer vision.  For example, HMMs are used in 
computer vision for motion analysis and activity understanding, while MRFs are 
extensively used for image labeling, segmentation, and stereo reconstruction. 
The latest research uses BNs in computer vision for representing causal 
relationships such as for facial expression recognition, active vision, visual 
surveillance, and for data mining and pattern discovery in pattern recognition. 
CRFs provide an appealing alternative to MRFs for supervised image segmentation 
and labeling, since they can easily incorporate expressive, non-local features. 
Another emerging trend is to use graphical models to integrate context and 
prior knowledge with visual cues in vision and multimedia systems.

Despite their importance and recent successes, PGMs' use in computer vision 
still has tremendous room to expand in scope, depth, and rigor.  Their use is 
especially important for robust and high level visual understanding and 
interpretation.  This special issue is dedicated to promoting systematic and 
rigorous use of PGMs for various problems in computer vision.  We are 
interested in applications of PGMs in all areas of computer vision , including 
(but not limited to)

       1) image and video modeling
       2) image and video segmentation
       3) object detection
       4) object and scene recognition
       5) high level event and activity understanding
       6) motion estimation and tracking
       7) new inference and learning (both structure and parameters) theories
          for graphical
          models arising in vision applications
       8) generative and discriminative models
       9) models incorporating contextual, domain, or commonsense knowledge


Tentative Timelines

August 16, 2008		Submission deadline
October 25, 2008        Notification of acceptance
April 18, 2009		Camera-ready manuscript due
October 1, 2009		Targeted publication date


Paper submission and review
The papers should be submitted online through PAMI manuscript central site, 
with a note/tag designating the manuscript to this special issue.  All 
submissions will be peer-reviewed by at least 3 experts in the field.  Priority 
will be given to work with high novelty and potential impacts.  We will return 
without review submissions that we feel are not well aligned with our goals for 
the special issue.