The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Scene Segmentation from Visual Motion Using Global Optimization
Abstract
This paper presents results from computer experiments with an algorithm to perform scene disposition and motion segmentation from visual motion or optic flow. The maximum a posteriori (MAP) criterion is used to formulate what the best segmentation or interpretation of the scene should be, where the scene is assumed to be made up of some fixed number of moving planar surface patches. The Bayesian approach requires, first, specification of prior expectations for the optic flow field, which here is modeled as spatial and temporal Markov random fields; and, secondly, a way of measuring how well the segmentation predicts the measured flow field. The Markov random fields incorporate the physical constraints that objects and their images are probably spatially continuous, and that their images are likely to move quite smoothly across the image plane. To compute the flow predicted by the segmentation, a recent method for reconstructing the motion and orientation of planar surface facets is used. The search for the globally optimal segmentation is performed using simulated annealing.
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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Research works printed but not linked: 39
- 10.1016/0167-8655(86)90028-0 · DOI
- 10.1016/0262-8856(86)90056-9 · DOI
- buxton, 1983, Monocular depth perception from optical flow by space time signal processing, Proc Roy Soc London B, 218, 27, 10.1098/rspb.1983.0024 · DOI
- murray, 1984, Reconstructing the optic flow field from edge motion: An examination of two different approaches, Proc First Conf Artif Intell Applications, 382
- 10.1016/0010-4655(85)90162-6 · DOI
- kearney, 1984, Gradient-based estimation of optical flow with global optimization, Proc First Conf Artif Intell Applications, 376
- marroquin, 1984, Surface Reconstruction Preserving Discontinuities
- kirkpatrick, 1983, Optimisation by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671 · DOI · Paper page
- 10.1016/0262-8856(84)90001-5 · DOI
- 10.1068/p090253 · DOI
- 10.1038/293133a0 · DOI · Paper page
- 10.1109/TPAMI.1984.4767471 · DOI · Paper page
- 10.1109/TCSI.2004.836856 · DOI
- buxton, 1984, 3D solutions to the aperture problem, ?roc 6th European Conf Artif Intell, 631
- 10.1177/027836498500400307 · DOI
- 10.1177/027836498500400306 · DOI
- 10.1109/TPAMI.1980.6447701 · DOI
- neumann, 1980, Motion analysis of image sequences for object grouping and reconstruction, Proc Pattern Recognition
- 10.1109/TPAMI.1985.4767678 · DOI · Paper page
- 10.1016/0004-3702(81)90024-2 · DOI · Paper page
- ullman, 1979, The Interpretation of Visual Motion, 10.7551/mitpress/3877.001.0001 · DOI · Paper page
- 10.1016/0734-189X(83)90098-1 · DOI
- proffitt, 1984, Converging approaches to extracting structure from motion: Psychophysical and computational investigations of recovering connectivity from moving point-light displays, ?roc First Conf Artif Intell Applications, 232
- 10.1109/C-M.1981.220559 · DOI
- schunck, 1984, Motion segmentation and estimation by constraint line clustering, Proc IEEE Workshop Computer Vision Representation and Control, 58
- 10.1016/0146-664X(79)90097-2 · DOI · Paper page
- 10.1016/0004-3702(84)90018-3 · DOI · Paper page
- 10.1016/S0734-189X(83)80026-7 · DOI · Paper page
- 10.3758/BF03212378 · DOI · Paper page
- 10.1364/JOSA.70.000060 · DOI
- 10.1037/h0056880 · DOI · Paper page
- 10.1016/0262-8856(85)90003-4 · DOI
- buxton, 1985, Scene segmentation as an ill-posed and maximum likelihood problem, Proc Alvey Comput Vision and Image Interpretation Meeting, 10.1016/0262-8856(85)90003-4 · DOI
- 10.1038/317314a0 · DOI · Paper page
- poggio, 1984, Ill-posed problems and regularization analysis in early vision
- besag, 1977, Spatial interaction and the statistical analysis of lattice systems (with discussion), J Roy Statist SOC ?, 36, 192
- 10.1109/TPAMI.1984.4767596 · DOI · Paper page
- 10.1016/S0734-189X(83)80037-1 · DOI
- 10.1016/S0734-189X(83)80030-9 · DOI · Paper page
Cited by
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
“Given more than two frames in the video, it is also possible to add temporal smoothness terms ∂u ∂t and ∂v ∂t to Equation (9) [10, 49, 54].”
BackgroundLucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
Bruhn, A., Weickert, J., & Schnörr, C. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000045324.43199.43 · Cited by 1,160
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
On the spatial statistics of optical flow
Roth, S., & Black, M. J. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.180 · Cited by 95
Metadata
- Authors
- Murray, D. W., & Buxton, B. F.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-03
- Volume
- PAMI-9
- Issue
- 2
- Pages
- 220-228
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W1997866406
- Cited by
- 300
- References
- 1
- FWCI
- 16.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Murray, D. W., & Buxton, B. F. (1987). Scene Segmentation from Visual Motion Using Global Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1987.4767896
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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).
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