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Analyzed frame pair instance (sorted)
RMSE [pixels]
WLSPR (64 features)
Ref. method (full support)
WLSPR (regular grid, 144 features)
Ref. method (regular grid, 12% support)
Figure 6: Comparison against the reference method.
Figure 7: Snapshots from the experiment with the Hand
sequence, Left: patch/object assignment, Middle: segmen-
tation masks computed from the WLSPR output, Right:
masks computed from the reference output.
cate that the object motion estimates provide the same
level of performance in post-processing.
4 CONCLUSIONS
In this paper, we have proposed an approach to extrac-
tion of background and foregroundmotions where the
temporal propagation of probabilistic feature associa-
tions is done. This is based on estimated displace-
ments which provides labeled seed points. Spatial
proximity of the new feature patches to those points
is then used to predict the labelling of features. This
propagation technique was integrated with iterative
refinement under the WLS estimation framework.
Experiments show that feature-based prediction of
motion provides a better starting point for segmen-
tation than the approach using dynamics. In addi-
tion, experiments show importance of using direc-
tional uncertainty information about the block motion
estimates in improving the precision and robustness
of the feature-based approach.
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