Motion Estimation for Regions of Reflections through Layer Separation

Regions of reflections contain two semi-transparent layers moving over each other. This generates two motion vectors per pel. Current multiple motion estimators either extend the usual brightness consistency assumption to two motions or are based on the Fourier phase shift relationship. Both approaches assume constant motion over at least three frames. As a result they can not handle temporally active motion due to camera shake or acceleration. This paper proposes a new approach for multiple motion estimation by modeling the correct motions as the ones generating the best layer separation of the examined reflection. A Bayesian framework is proposed which then admits a solution using candidate motions generated from KLT trajectories and a layer separation technique. We use novel temporal priors and our results show handling of strong motion inconsistencies and improvements over previous work.

[1]  Vladimir Kolmogorov,et al.  Optimizing Binary MRFs via Extended Roof Duality , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Patrick Bouthemy,et al.  Motion-Based Segmentation of Transparent Layers in Video Sequences , 2006, MRCS.

[3]  David Vernon,et al.  Decoupling Fourir Components of Dynamic Image Sequences: A Theory of Signal Separation, Image Segmentation, and Optical Flow Estimation , 1998, ECCV.

[4]  Denis Pellerin,et al.  Motion estimation of transparent objects in the frequency domain , 2004, Signal Process..

[5]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[6]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Til Aach,et al.  Divide-and-Conquer Strategies for Estimating Multiple Transparent Motions , 2004, IWCM.

[8]  Kenji Mase,et al.  Unified computational theory for motion transparency and motion boundaries based on eigenenergy analysis , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Yair Weiss,et al.  Deriving intrinsic images from image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  A. Kokaram Motion picture restoration , 1998 .

[11]  Til Aach,et al.  Analysis of Superimposed Oriented Patterns , 2006, IEEE Transactions on Image Processing.

[12]  Anil C. Kokaram,et al.  Reflection detection in image sequences , 2011, CVPR 2011.

[13]  Michal Irani,et al.  Separating transparent layers of repetitive dynamic behaviors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Til Aach,et al.  Multiple-Motion-Estimation by Block-matching using MRF , 2004 .

[15]  Rubén Medina,et al.  Multiple motion estimation and segmentation in transparency , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[16]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.