TPAMI 2013 : Special Issue on “Higher Order Graphical Models in Computer Vision: Modelling, Inference & Learning”
Call For Papers
Call for Papers
Special Issue on “Higher Order Graphical Models in Computer Vision: Modelling, Inference & Learning”
To be published in IEEE Transactions on Pattern Analysis and Machine Intelligence
Recently, there has been an increasing interest in modelling image priors with higher-order models and global constraints. These models have the ability to encode significantly more sophisticated priors and structural dependencies among image pixels, compared to the traditional pairwise interactions. Examples of such models in computer vision include, second-order smoothness priors in stereo, priors on natural image statistics for de-noising, robust smoothness priors for object labelling, co-occurrence priors for object category segmentation, connectivity and bounding-box priors for segmentation.
This special issue invites original articles addressing the issues of modelling, inference, and learning in models with higher-order terms and global constraints. We also welcome survey and overview papers in these general areas. Specific topics of interest include, but are not limited to:
(a) Different forms of higher order potentials
(b) Grammar-based models
(c) Which image priors should be modelled?
(d) Extending the class of exactly solvable higher-order models
(e) Approximate inference in higher-order models
(f) Theoretical upper bound for the approximate solutions
(g) Learning with higher-order potentials and global constraints
(h) Piecewise or distributed or coarse-to-fine learning.
Papers must be submitted online to the TPAMI submission site: https://mc.manuscriptcentral.com/tpami-cs. Complete manuscripts formatted according to TPAMI guidelines, available at http://www.computer.org/portal/web/peerreviewjournals/author, are expected.
Submission Deadline: April 1, 2013
Reviews: October 1, 2013
Revisions of Submissions: January 1, 2014
Final Decisions/Manuscript: April 1, 2014
Estimated Online Publication: Fall 2014