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Motion magnification

Published: 01 July 2005 Publication History

Abstract

We present motion magnification, a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified. After an initial image registration step, we measure motion by a robust analysis of feature point trajectories, and segment pixels based on similarity of position, color, and motion. A novel measure of motion similarity groups even very small motions according to correlation over time, which often relates to physical cause. An outlier mask marks observations not explained by our layered motion model, and those pixels are simply reproduced on the output from the original registered observations.The motion of any selected layer may be magnified by a user-specified amount; texture synthesis fills-in unseen "holes" revealed by the amplified motions. The resulting motion-magnified images can reveal or emphasize small motions in the original sequence, as we demonstrate with deformations in load-bearing structures, subtle motions or balancing corrections of people, and "rigid" structures bending under hand pressure.

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References

[1]
Arikan, O., and Forsyth, D. A. 2002. Synthesizing constrained motions from examples. ACM Transactions on Graphics 21, 3 (July), 483--490.
[2]
Boykov, Y., Veksler, O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Pat. Anal. Mach. Intell. 23, 11, 1222--1239.
[3]
Brand, M., and Hertzmann, A. 2000. Style machines. In Proceedings of ACM SIGGRAPH 2000, 183--192.
[4]
Brostow, G., and Essa, I. 1999. Motion-based video decompositing. In IEEE International Conference on Computer Vision (ICCV '99), 8--13.
[5]
Brostow, G., and Essa, I. 2001. Image-based motion blur for stop motion animation. In Proceedings of ACM SIGGRAPH 2001, 561--566.
[6]
Dempster, A. P., Laird, N. M., and Rubin. D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statist. Soc. B 39, 1--38.
[7]
Efros, A. A., and Leung, T. K. 1999. Texture synthesis by non-parametric sampling. In IEEE International Conference on Computer Vision, 1033--1038.
[8]
Fischler, M., and Bolles, R. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6, 381--395.
[9]
Gleicher, M. 1998. Retargetting motion to new characters. In Proceedings of ACM SIGGRAPH 98, 33--42.
[10]
Harris, C., and Stephens, M. 1988. A combined corner and edge detector. In Proceedings of 4th Alvey Vision Conference, 147--151.
[11]
Jojic, N., and Frey, B. 2001. Learning flexible sprites in video layers. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'01), 199--206.
[12]
Lee, J., Chai, J., Reitsma, P. S. A., Hodgins, J. K., and Pollard, N. S. 2002. Interactive control of avatars animated with human motion data. ACM Transactions on Graphics 21, 3 (July), 491--500.
[13]
Li, Y., Wang, T., and Shum, H.-Y. 2002. Motion texture: A two-level statistical model for character motion synthesis. ACM Transactions on Graphics 21, 3 (July), 465--472.
[14]
Lucas, B., and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision. In Image Understanding Workshop, 121--130.
[15]
Nobel, A. 1989. Descriptions of Image Surfaces. PhD thesis, Oxford University, Oxford, UK.
[16]
Pullen, K., and Bregler, C. 2002. Motion capture assisted animation: Texture and synthesis. ACM Transactions on Graphics 21 (July), 501--508.
[17]
Rother, C., Kolmogorov, V., and Blake, A. 2004. Interactive foreground extraction using iterated graph cuts. In Proceedings of ACM SIGGRAPH 2004, 309--314.
[18]
Ruzon, M., and Tomasi. C. 2000. Alpha estimation in natural images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'00), 24--31.
[19]
Sand, P., and Teller, S. 2004. Video matching. In Proceedings of ACM SIGGRAPH 2004, 592--599.
[20]
Sawhney, H., Guo, Y., Hanna, K., Kumar, R., Adkins, S., and Zhou, S. 2001. Hybrid stereo camera: An ibr approach for synthesis of very high resolution stereoscopic image sequences. In Proceedings of ACM SIGGRAPH 2001, 451--460.
[21]
Schodl, A., Szeliski, R., Salesin, D., and Essa, I. 2000. Video textures. In Proceedings of ACM SIGGRAPH 2000, 489--498.
[22]
Shi, J., and Malik, J. 1998. Motion segmentation and tracking using normalized cuts. In Proceedings of International Conference on Computer Vision, 1154--1160.
[23]
Shi. J., and Tomasi, C. 1994. Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), 593--600.
[24]
Strang, G. 1986. Introduction to Applied Mathematics. Wellesley-Cambridge Press.
[25]
Torr, P. 1998. Philosophical Transactions of the Royal Society. Roy Soc, ch. Geometric Motion Segmentation and Model Selection, 1321--1340.
[26]
Unuma, M., Anjyo, K., and Takeuchi, R. 1995. Fourier principles for emotion-based human figure animation. In Proceedings of ACM SIGGRAPH 95, 91--96.
[27]
Verma, D., and Meila, M. 2003. A comparison of spectral methods. Tech. Rep. UW-CSE-03-05-01, ept. of Computer Science and Engineering, University of Washington.
[28]
Wang, J., and Adelson, E. 1994. Representing moving images with layers. IEEE Trans. Image Processing 3, 5, 625--638.
[29]
Weiss, Y., and Adelson, E. 1994. Perceptual organized EM: A framework for motion segmentation that combines information about form and motion. Tech. rep., MIT Media Laboratory Perceptual Computing Section Technical Report No. 315.
[30]
Weiss, Y. 1997. Smoothness in Layers: Motion segmentation using nonparametric mixture estimation. In Proceedings of Computer Vision and Pattern Recognition, 520--527.
[31]
Wills, J., Agarwal, S., and Belongie, S. 2003. What went where. In Proceedings of Computer Vision and Pattern Recognition, 37--44.
[32]
Zelnik-Manor, L., and Irani, M. 2003. Degeneracies, dependencies and their implications in multi-body and multi-sequence factorizations. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'03), 287--293.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 24, Issue 3
July 2005
826 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/1073204
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 July 2005
Published in TOG Volume 24, Issue 3

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Author Tags

  1. computer vision
  2. motion processing
  3. video processing
  4. video-based rendering

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  • (2024)Ultrasound B-Mode Visualization of Imperceptible Subwavelength Vibration in Magnetomotive Ultrasound ImagingVibration10.3390/vibration70300407:3(764-775)Online publication date: 12-Jul-2024
  • (2024)A Method for Directly Observing Mechanical Oscillations in Photonic Structures Based on Porous Silicon NanostructuresMicro10.3390/micro40100064:1(80-96)Online publication date: 1-Feb-2024
  • (2024)Improved video motion magnification method assisted by digital image correlationInternational Conference on Optical and Photonic Engineering (icOPEN 2023)10.1117/12.3023281(43)Online publication date: 15-Feb-2024
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