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Hierarchical model-based motion estimation
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Cites
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Research works printed but not linked: 22
- G. Adiv. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Trans. on Pattern Analysis and Machine Intelligence, 7(4):384–401, July 1985. · DOI · Paper page
- P. Anandan. A unified perspective on computational techniques for the measurement of visual motion. In International Conference on Computer Vision, pages 219–230, London, May 1987.
- P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2:283–310, 1989. · DOI · Paper page
- J. R. Bergen and E. H. Adelson. Hierarchical, computationally efficient motion estimation algorithm. J. Opt. Soc. Am. A., 4:35, 1987.
- J. R. Bergen, P. J. Burt, R. Hingorani, and S. Peleg. Computing two motions from three frames. In International Conference on Computer Vision, Osaka, Japan, December 1990.
- P.J. Burt, J.R. Bergen, R. Hingorani, R. Kolczinski, W.A. Lee, A. Leung, J. Lubin, and H. Shvaytser. Object tracking with a moving camera, an application of dynamic motion analysis. In IEEE Workshop on Visual Motion, pages 2–12, Irvine, CA, March 1989. · DOI · Paper page
- P.J. Burt, R. Hingorani, and R. J. Kolczynski. Mechanisms for isolating component patterns in the sequential analysis of multiple motion. In IEEE Workshop on Visual Motion, pages 187–193, Princeton, NJ, October 1991. · DOI
- Stefan Carlsson. Object detection using model based prediction and motion parallax. In Stockholm workshop on computational vision, Stockholm, Sweden, August 1989.
- J. Dengler. Local motion estimation with the dynamic pyramid. In Pyramidal systems for computer vision, pages 289–298, Maratea, Italy, May 1986. · DOI
- W. Enkelmann. Investigations of multigrid algorithms for estimation of optical flow fields in image sequences. Computer Vision, Graphics, and Image Processing, 4339:150–177, 1988. · DOI
- K. J. Hanna. Direct multi-resolution estimation of ego-motion and structure from motion. In Workshop on Visual Motion, pages 156–162, Princeton, NJ, October 1991. · DOI
- J. Heel. Direct estimation of structure and motion from multiple frames. Technical Report 1190, MIT AI LAB, Cambridge, MA, 1990.
- E. C. Hildreth. The Measurement of Visual Motion. The MIT Press, 1983.
- B. K. P. Horn. Robot Vision. MIT Press, Cambridge, MA, 1986.
- B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185–203, 1981. · DOI · Paper page
- B. K. P. Horn and E. J. Weldon. Direct methods for recovering motion. International Journal of Computer Vision, 2(1):51–76, June 1988. · DOI
- B.D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Image Understanding Workshop, pages 121–130, 1981.
- L. Matthies, R. Szeliski, and T. Kanade. Kalman filter-based algorithms for estimating depth from image-sequences. In International Conference on Computer Vision, pages 199–213, Tampa, FL, 1988.
- H. H. Nagel. Displacement vectors derived from second order intensity variations in intensity sequences. Computer Vision, Pattern recognition and Image Processing, 21:85–117, 1983. · DOI · Paper page
- S. Negahdaripour and B.K.P. Horn. Direct passive navigation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 9(1):168–176, January 1987. · DOI
- A. Singh. An estimation theoretic framework for image-flow computation. In International Conference on Computer Vision, Osaka, Japan, November 1990.
- A.M. Waxman and K. Wohn. Contour evolution, neighborhood deformation and global image flow: Planar surfaces in motion. International Journal of Robotics Research, 4(3):95–108, Fall 1985. · DOI · Paper page
Cited by
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“Another set of examples are parametric motion algorithms [9], which also just solve for a small number of unknowns.”
BackgroundInfluentialAutomatic facial expression analysis: a survey
Fasel, B., & Luettin, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00052-3 · Cited by 1,810
A Survey on the Edge Computing for the Internet of Things
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520
Metadata
- Authors
- Bergen, J. R., Anandan, P., Hanna, K. J., & Hingorani, R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1992
- Pages
- 237-252
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1938714998
- Cited by
- 1,154
- References
- 1
- FWCI
- 33.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- CoChat. (2026). Citation check: Hierarchical model-based motion estimation. https://app.cochat.ai/papers/10.1007/3-540-55426-2_27
- The paper
- Bergen, J. R., Anandan, P., Hanna, K. J., & Hingorani, R. (1992). Hierarchical model-based motion estimation. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-55426-2_27
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
