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
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.
More and the latest information about the special issue may be found at
http://www.ecse.rpi.edu/homepages/qji/PAMI_GM.html
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