The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The computation of optical flow
Abstract
Two-dimensional image motion is the projection of the three-dimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of time-orderedimages allow the estimation of projected two-dimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field. Provided that optical flow is a reliable approximation to two-dimensional image motion, it may then be used to recover the three-dimensional motion of the visual sensor (to within a scale factor) and the three-dimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the three-dimensional environment, and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement. We investigate the computation of optical flow in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use. The survey concludes with a discussion of current research issues.
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Cites
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
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
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Watson, A. B., & Ahumada, A. J. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000322 · Cited by 1,170
Computation of component image velocity from local phase information
Fleet, D. J., & Jepson, A. D. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056772 · Cited by 1,085
Optical flow using spatiotemporal filters
Heeger, D. J. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133568 · Cited by 534
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Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
Deepfake Video Detection through Optical Flow Based CNN
Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00152 · Cited by 424
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices
Chen, T. Y.-H., Ravindranath, L., Deng, S., Bahl, P., & Balakrishnan, H. (2015) · Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2809695.2809711 · Cited by 358
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Mao, J., Xiao, T., Jiang, Y., & Cao, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.639 · Cited by 306
Vision-Based Fall Detection with Convolutional Neural Networks
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Metadata
- Authors
- Beauchemin, S. S., & Barron, J. L.
- Venue
- ACM Computing Surveys
- Published
- 1995-09
- Volume
- 27
- Issue
- 3
- Pages
- 433-466
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2093488518
- Cited by
- 1,278
- References
- 5
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- closed
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