The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Digital video processing
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Moving Shadow and Object Detection in Traffic Scenes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
| Hi-index | 0.00 |
This paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.