Learning in graphical models
Human motion analysis: a review
Computer Vision and Image Understanding
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
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Discrimination between the moving foreground objects and the complex dynamic background is a challenging task. In this paper, we have proposed a probabilistic graphical model – a recurrent stochastic network, which is able to learn the temporal and the spatial correlation from the video input data and make inference with a generalized belief propagation algorithm. Experiments have shown that the proposed recurrent network can model the dynamic backgrounds containing swaying trees, bushes and moving ocean waves. Very promising segmentation results have been obtained.