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An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences
Abstract
A mapping between one frame from an image sequence and the preceding or following frame can be represented as a displacement vector field. In most situations, the mere gray value variations do not provide sufficient information in order to estimate such a displacement vector field. Supplementary constraints are necessary, for example the postulate that a displacement vector field varies smoothly as a function of the image position. Taken as a general requirement, this creates difficulties at gray value transitions which correspond to occluding contours. Nagel therefore introduced the ``oriented smoothness'' requirement which restricts variations of the displacement vector field only in directions with small or no variation of gray values. This contribution reports results of an investigation about how such an ``oriented smoothness'' constraint may be formulated and evaluated.
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Research works printed but not linked: 29
- 1984, Informatik-Fachberichte, 87, 154, 10.1007/978-3-662-02390-7_21 · DOI
- 10.1007/978-3-662-02390-7_21 · DOI
- 0, Proc IJCAI-81, 716
- 10.1016/S0734-189X(83)80040-1 · DOI
- 10.1007/978-3-642-81935-3_23 · DOI
- 10.1007/978-3-642-81935-3_23 · DOI
- 10.1016/0004-3702(84)90018-3 · DOI · Paper page
- 10.1016/0004-3702(81)90024-2 · DOI · Paper page
- kitchen, 1980, Gray-level corner detection
- 10.1016/0167-8655(82)90020-4 · DOI
- 10.1016/0167-8655(82)90052-6 · DOI
- 10.1016/S0734-189X(83)80030-9 · DOI · Paper page
- nagel, 1983, On the estimation of dense displacement vector fields from image sequences, Proc ACM SIGGRAPH/SIGART Interdisciplinary Workshop on Motion Representation and Perception, 59
- 1984, Recent advances in image sequence analysis, Proc Premier Colloque Image?Traitement Synth�se Technologie et Applications, 545
- 10.1016/0146-664X(82)90081-8 · DOI · Paper page
- 10.1016/0734-189X(83)90029-4 · DOI
- 10.1016/0146-664X(82)90081-8 · DOI · Paper page
- 10.1145/988525.988534 · DOI
- hildreth, 1982, The integration of motion information along contours, Proc IEEE Workshop Computer Vision Representation and Control, 83
- cornelius, 1983, Adapting optical flow to measure object motion in reflectance and X-ray image sequences, Proc ACM SIGGRAPH/SIGART Workshop in Motion Representation and Perception, 50
- hildreth, 1983, The Measurement of Visual Motion
- beaudet, 1978, Rotationally invariant image operators, Proc Int Joint Conf Pattern Recognition, 579
- 0, Progress in Image Processing, 222
- nagel, 1984, Spatio-temporal modeling based on image sequences, Proc Int Symp Image Processing and Its Applications
- nagel, 1982, Investigations of second order gray value variations to estimate corner point displacements, Proc Int Conf Pattern Recognition, 768
- nagel, 1984, Towards the estimation of displacement vector fields by 'oriented smoothness' constraints, Proc Int Conf Pattern Recognition, 6
- nagel, 1983, Iterative estimation of displacement vector fields from TV-frame sequences, Proc Second European Signal Processing Conf (EUSIPCO-83), 299
- wu, 1982, Determining velocities by propagation, Proc Int Conf Pattern Recognition, 1147
- zuniga, 1983, Corner detection using the facet model, Proc IEEE Conf Comput Vision and Pattern Recognition, 30
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Metadata
- Authors
- Nagel, H.-H., & Enkelmann, W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1986-09
- Volume
- PAMI-8
- Issue
- 5
- Pages
- 565-593
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2031555400
- Cited by
- 786
- References
- 0
- FWCI
- 24.69
- Type
- journal article
- Language
- en
- Open access
- closed
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