Image analysis – Applications – Motion or velocity measuring
Reexamination Certificate
2006-01-05
2011-11-22
Azarian, Seyed (Department: 2624)
Image analysis
Applications
Motion or velocity measuring
C382S291000, C375S240270
Reexamination Certificate
active
08064644
ABSTRACT:
This invention relates to digital image and video processing. It is concerned with determining the motion of pixels between frames in an image sequence. That motion relates to the motion of objects in the scene. The invention determines two kinds of motion information at each pixel site. The first kind of information is the displacement between a pixel site in the current frame and the corresponding site in me previous or next frame. This is the motion vector at that site. The second is an indication of whether that pixel is occluded or not in the previous or next frames. This allows any subsequent processing to take advantage of the fact that the pixel in the current frame may have been covered in the next (or previous) frame by some object moving over it (or revealing it). The invention also allows for prior knowledge of objects that are to be ignored in the process of motion determination.
REFERENCES:
patent: 6438170 (2002-08-01), Hackett et al.
patent: 6466624 (2002-10-01), Fogg
patent: 6487313 (2002-11-01), De Haan et al.
patent: 7085401 (2006-08-01), Averbuch et al.
patent: 7343044 (2008-03-01), Baba et al.
patent: 7697769 (2010-04-01), Baba et al.
patent: 0477616 (1992-04-01), None
patent: 9922520 (1999-05-01), None
patent: 2004082294 (2004-09-01), None
I Kokaram, et al., “A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors”, Computer Vision-ECCV '96. 4lh European Conference on Computer Proceedings, vol. 2, pp. 613-624, 1996.
J. Besag. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society B, 48:259-302, 1986.
M.J. Black and P. Anandan. A framework for the robust estimation of optical flow. In Fourth International Conf. on Computer Vision, pp. 231-236, May 1993.
P. Bouthemy, M. Gelgon, and F. Ganansia. A unified approach to shot change detection and camera motion characterization. IEEE Transactions on Circuits and Systems for Video Technology, 9:1030-1044, 1999.
H. Denman, N. Rea, and A. Kokaram. Content based analysis for video from snooker broadcasts. In International Conference on Image and Video Retrieval CIVR2002, Jul. 18-19, 2002.
D.P. Elias and N.G. Kingsbury. An efficient block segmentation algorithm for true motion estimation. In Sixth IEEE International Conference on Image Processing and Its Applications, vol. 1, pp. 209-213. IEEE Conference Publications 443, Jul. 14-17, 1997.
M. Ghanbari. The cross-search algorithm for motion estimation. IEEE Transactions on Communication, 38:950-953, Jul. 1990.
H. Higgins. The interpretation of a moving retinal image. Proceedings of the Royal Society, London, B 208:385-397, 1980.
B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
D.M. Martinez. Model-based motion estimation and its application to restoration and interpolation of motion pictures. PhD thesis, Massachusetts Institute of Technology, 1986.
J. Biemond, L. Looijenga, D.E. Boekee, and R.H.J.M. Plompen. A pel-recursive Wiener-based displacement estimation algorithm. Signal Processing. 13:399-412,1987.
L. Boroczy, J.N. Driessen, and J. Biemond. Adaptive algorithms for pel-recursive displacement estimation. In Proceedings SPIE VCIP, pp. 1210-1221, 1990.
H. Higgins. The visual ambiguity of a moving plane. Proceedings of the Royal Society, London, B 223:165-175, 1984.
V. Seferidis and M. Ghanbari. Heirarchical motion estimation using texture analysis. In IEEE 4th Conference on Image Processing, pp. 61-64, 1992.
M. Bierling. Displacement estimation by heirarchical block matching. In SPIE VCIP, pp. 942-951, 1988.
A.C. Kokaram. Motion Picture Restoration: Digital Algorithms for Artefact Suppression in Degraded Motion Picture Film and Video. Springer Verlag, ISBN 3-540-76040-7, pp. 13-46. 1998.
A. Murat Tekalp. Digital Video Processing. pages 72-94, 177-197. Prentice Hall, 1995.
A. Zaccarin and B, Liu. Fast Algorithms for Block Motion Estimation. In IEEE ICASSP, vol. 3, pp. 449-452, 1992.
Kokarama, “On Missing Data Treatment for Degraded Video and Film Archives: A Survey and a New Bayesian Approach”, IEEE Transactions on Image Processing , vol. 13, No. 3, pgs. 397-415, 2004.
Kokaram, et al., “Automated Rig Removal with Bayesian Motion Interpolation”, IEE Conference Publication; 1stEuropean conference on Visual Media Production, pp. 155-164, 2004.
Black, et al., “Robust Dynamic Motion Estimation Over Time”, IEEE Computer Society Conference on Computer Visison and Pattern Recognition, pgs. 296-302, 1991.
Kokaram, et al., “A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors”, Computer Vision-ECCV '96. 4thEuropean Conference on Computer Proceedings, vol. 2 , pgs. 613-624, 1996.
Kelly, et al., “Graphics Hardware for Gradient Based Motion Estimation”, Proceedings of SPIE-IS&T Electronic Imaging, vol. 5309, No. 1, pp. 92-103, 2004.
Patent Cooperation Treaty Written Opinion of the International Searching Authority and International Search Report for International Application No. PCT/IE2006/000001 mailed on Aug. 2, 2006.
Azarian Seyed
Google Inc.
Young Basile Hanlon & MacFarlane P.C.
LandOfFree
Method for estimating motion and occlusion does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method for estimating motion and occlusion, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for estimating motion and occlusion will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4280829