Elements of information theory
Elements of information theory
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale annealing for grouping and unsupervised texture segmentation
Computer Vision and Image Understanding
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Graph Partition by Swendsen-Wang Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 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
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking appearances with occlusions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
A decentralized probabilistic approach to articulated body tracking
Computer Vision and Image Understanding
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Mean field approach for tracking similar objects
Computer Vision and Image Understanding
Iterated conditional modes for inverse dithering
Signal Processing
Estimating 3D pose via stochastic search and expectation maximization
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
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This paper proposes a novel probabilistic variational method with deterministic annealing for the maximum a posteriori (MAP) estimation of complex stochastic systems. Since the MAP estimation involves global optimization, in general, it is very difficult to achieve. Therefore, most probabilistic inference algorithms are only able to achieve either the exact or the approximate posterior distributions. Our method constrains the mean field variational distribution to be multivariate Gaussian. Then, a deterministic annealing scheme is nicely incorporated into the mean field fix-point iterations to obtain the optimal MAP estimate. This is based on the observation that when the covariance of the variational Gaussian distribution approaches to zero, the infimum point of the Kullback-Leibler (KL) divergence between the variational Gaussian and the real posterior will be the same as the supreme point of the real posterior. Although global optimality may not be guaranteed, our extensive synthetic and real experiments demonstrate the effectiveness and efficiency of the proposed method.