Segmentation of moving objects by robust motion parameter estimation over multiple frames
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Dependence in the Observation of Visual Contours
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Motion Segmenation and Depth Ordering Based on Morphological Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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This paper presents a new Bayesian framework for layered motion segmentation, dividing the frames of an image sequence into foreground and background layers by tracking edges. The first frame in the sequence is segmented into regions using image edges, which are tracked to estimate two affine motions. The probability of the edges fitting each motion is calculated using 1st order statistics along the edge. The most likely region labelling is then resolved using these probabilities, together with a Markov Random Field prior. As part of this process one of the motions is also identified as the foreground motion. Good results are obtained using only two frames for segmentation. However, it is also demonstrated that over multiple frames the probabilities may be accumulated to provide an even more accurate and robust segmentation. The final region labelling can be used, together with the two motion models, to produce a good segmentation of an extended sequence.