Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model structure and reliable inference
Perception as Bayesian inference
Empirical Bayesian EM-based Motion Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Layered Motion Representation with Occlusion and Compact Spatial Support
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Bayesian inference of visual motion boundaries
Exploring artificial intelligence in the new millennium
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic acquisition and initialization of articulated models
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
The MPM-MAP algorithm for motion segmentation
Computer Vision and Image Understanding
Region and Graph-Based Motion Segmentation
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A bayesian approach to situated vision
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Dense optic flow with a bayesian occlusion model
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
A novel video salient object extraction method based on visual attention
Image Communication
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We introduce an empirical Bayesian procedure for the simultaneous segmentation of an observed motion field and estimation of the hyperparameters of a Markov random field prior. The new approach exhibits the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement for a quantitative specification of its parameters. This eliminates the need for trial-and-error strategies for the determination of these parameters and leads to better segmentations.