Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Framework for Rigid and Non-Rigid Appearance Based Tracking and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cyclic articulated human motion tracking by sequential ancestral simulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fundamental performance limits in image registration
IEEE Transactions on Image Processing
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Most previous super-resolution (SR) approaches are implemented with two individual cascade steps, image registration and image fusion, which handicaps the incorporation of the structural information of the objects of interest, e.g. human faces, into SR in a parallel way. This prior information is beneficial to either robust motion estimation or fusion with higher quality. In this paper, SR reconstruction is formulated as Bayesian state estimation of location and appearance parameters of a face model. In addition, a sequential Monte Carlo (SMC) based algorithm is proposed to achieve the probabilistic state estimation, i.e. SR reconstruction in our formulation. Image alignment and image fusion are combined into one unified framework in the proposed approach, in which the prior information from the face model is incorporated into both registration and fusion process of SR. Experiments performed on synthesized frontal face sequences show that the proposed approach gains superior performance in registration as well as reconstruction.