Hierarchical joint estimation/segmentation of optic-flow
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Contacts: E Mémin, P
Pérez
We address here the intricate issue of jointly recovering the
apparent velocity field between two consecutive frames and its underlying
partition. The joint estimator we designed is modelized as the minimization of a global cost functional including robust estimators. These estimators
have two main advantages. First of all, they enable the method to be robust to
the large deviations occurring in the different terms composing our energy
function. Secondly, they also offer the possibility to introduce a simple
coupling between a dense optical flow field and a segmentation process through
auxiliary variables. We reinforce also this coupling by a parametric
likeness term applied on each
regions of the partition. The underlying minimization at hand is conducted
efficiently by a multigrid optimization algorithm.
The resulting estimation-segmentation model operates finally a tight cooperation between a local estimation process and a global modelization.}
We presents here results obtained for a parking lot sequence:
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Segmentation results at the different grid level
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Parametric motion field
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Dense vector fields
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We presents also a movi showing the evolution of the hierarchical segmentation on the yosemite sequence:
![Segmentation](yos_seg.gif) |
evolution of the segmentation |
The table below gathers the results obtained on the basis of Barron
et. al criterion ("performance of optical flow techniques" IJCV vol. 12 1994), for the cropped Yosemite sequence (without the sky). This criterion represents a discrepancy between the estimated flow field and the actual one.
Motion field |
Mean angular error |
Standard deviation |
Parametric |
1.58 |
1.21 |
Dense |
1.92 |
1.59 |
Results obtained with other methods are recalled below:
Estimateur |
Résultats |
Références |
Szeliski et Coughlan |
moy= 2.45 sigma= 3.05 |
"Hierarchical spline based image registration". CVPR'94 |
Black et Anandan |
moy= 4.46 sigma= 4.21 |
"Robust incremental optical flow", CVPR'92 |
Black |
moy= 3.52 sigma= 3.25 |
"Recursive non linear estimation of discontinuous flow field", ECCV'94 |
Black et Jepson |
moy= 2.29 sigma= 2.25 |
"Estimating optical flow in segmented images using variable-order parametric models with local deformations", PAMI vol.
18, 1996 |
Ju, Black et Jepson |
moy= 2.16 sigma= 2.0 |
"Skin and bones: multi-layer locally affine, optical flow and regularization with transparency", CVPR'96 |
Lai et Vemuri |
moy= 1.99 sigma= 1.41 |
"Reliable and efficient computation of optical flow", IJCV 29(2) 1998 |
- E. Mémin, P. Pérez. Joint estimation-segmentation of optic flow. Proc. of the 5th European Conf. on Computer Vision, ECCV'98, Volume 2, pages 563-580, Freiburg, Germany, June 1998. (postscript)
- E. Mémin et P. Pérez. Hierarchical estimation and segmentation of dense motion fields. submitted to Int. Journ of Comp. Vision. (postscript)