Empirical Bayesian Motion Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstructing 3D trajectories of independently moving objects using generic constraints
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Tracking based motion segmentation under relaxed statistical assumptions
Computer Vision and Image Understanding
Tracking based motion segmentation under relaxed statistical assumptions
Computer Vision and Image Understanding
Realistic modeling of water droplets for monocular adherent raindrop recognition using Bézier curves
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Statistical unbiased background modeling for moving platforms
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
Computer Vision and Image Understanding
| Hi-index | 0.00 |
A recent trend in motion-based segmentation has been to rely on statistical procedures derived from Expectation-Maximization (EM) principles. EM-based approaches have various attractives for segmentation, such as proceeding by taking non-greedy soft decisions with regards to the assignment of pixels to regions, or allowing the use of sophisticated priors capable of imposing spatial coherence on the segmentation. A practical difficulty with such priors is, however, the determination of appropriate values for their parameters. In this work, we exploit the fact that the EM framework is itself suited for empirical Bayesian data analysis to develop an algorithm that finds the estimates of the prior parameters which best explain the observed data. Such an approach maintains the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement of a quantitative specification of its parameters. This eliminates the need for trial-and-error strategies for parameter determination and leads to better segmentations in less iterations.