The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Outlier detection and motion segmentation:
Abstract
We present a new method for solving the problem of motion segmentation, identifying the objects within an image moving independently of the background. We utilize the fact that two views of a static 3D point set are linked by a 3 X 3 Fundamental Matrix (F). The Fundamental Matrix contains all the information on structure and motion from a given set of point correspondences and is derived by a least squares method under the assumption that the majority of the image is undergoing a rigid motion. Least squares is the most commonly used method of parameter estimation in computer vision algorithms. However the estimated parameters from a least squares fit can be corrupted beyond recognition in the presence of gross errors or outliers which plague any data from real imagery. Features with a motion independent of the background are those statistically inconsistent from the calculated value of (F). Well founded methods for detecting these outlying points are described.
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Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Fitting conic sections to "very scattered" data: An iterative refinement of the bookstein algorithm
Sampson, P. D. (1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90101-0 · Cited by 358
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
In defense of the eight-point algorithm
Hartley, R. I. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.601246 · Cited by 1,625
In defence of the 8-point algorithm
Hartley, R. I. (2002) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466816 · Cited by 649
Randomized RANSAC with T, test
Matas, J., & Chum, O. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.009 · Cited by 281
Metadata
- Authors
- Torr, P. H. S., & Murray, D. W.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 1993-08-20
- Volume
- 2059
- Pages
- 432-443
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2200154090
- Cited by
- 205
- References
- 2
- FWCI
- 11.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Torr, P. H. S., & Murray, D. W. (1993). Outlier detection and motion segmentation:. SPIE Proceedings. https://doi.org/10.1117/12.150246
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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.
