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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computational Framework for Segmentation and Grouping
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Self-Organized Integration of Adaptive Visual Cues for Face Tracking
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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Democratic Integration: A Theory of Adaptive Sensory Integration
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
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A model for figure-ground segmentation by self-organized cue integration
Motion segmentation with accurate boundaries: a tensor voting approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Biological and cognitive foundations of intelligent sensor fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, “democratic” fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://organic.usc.edu:8376/∼tangx/neco/index.html.)