Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Simultaneous Two-View Epipolar Geometry Estimation and Motion Segmentation by 4D Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Accurate Motion Layer Segmentation and Matting
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
PETS Metrics: On-Line Performance Evaluation Service
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Segmenting, modeling, and matching video clips containing multiple moving objects
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Representing moving images with layers
IEEE Transactions on Image Processing
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This paper presents a novel technique to generate regions of interest in image sequences containing independent motions. The technique uses a novel motion segmentation method to segment optical flow using a local entropies field. Local entropy values are computed for each optical flow vector and are collected as input for a two state Markov Random Field that is used to discriminate the motion boundaries. Local entropy values are highly informative cues on the amount of information contained in the vector's neighborhood. High values represent significative motion differences, low values express uniform motions. For each cluster a motion model is fitted and it is used to create a multiple hypothesis prediction for the following frame. Experiments have been performed on standard and outdoor datasets in order to show the validity of the proposed technique.