A parallel algorithm for real-time computation of optical flow
Abstract
The precise management of two-dimensional field of velocities from time-varying two-dimensional images is impossible in general. It is, however, possible to compute suitable 'optical flows' that are qualitatively similar to the velocity field in most cases. We describe a simple, parallel algorithm that computes an optical flow from sequences of real images, which is consistent with human psychophysics and suggests plausible physiological models. In particular, our algorithm runs on a Connection Machine supercomputer in close-to-real time. It shows several of the same 'illusions' that are perceived by humans. A natural physiological implementation of the model is consistent with data from cortical areas V1 and MT.
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This type of simple, local calculation could potentially be implemented in genetically-specified and/or spontaneously self-organized neural circuitry in the retina or early dorsal visual areas.
“This type of simple, local calculation could potentially be implemented in genetically-specified and/or spontaneously self-organized neural circuitry in the retina or early dorsal visual areas (Bülthoff et al., 1989), perhaps similarly to the frog's “bug detector.””
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Bülthoff et al., 1989
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- Authors
- Bülthoff, H., Little, J., & Poggio, T.
- Affiliations
- Max Planck Society (DE)
- Massachusetts Institute of Technology (US)
- Venue
- Nature
- Published
- 1989-02
- Volume
- 337
- Issue
- 6207
- Pages
- 549-553
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2017694161
- Cited by
- 158
- References
- 15
- Citations by year
- 2023: 2 · 2020: 1 · 2016: 5 · 2015: 4 · 2014: 4 · 2013: 1 · 2012: 2
- FWCI
- 18.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- optical flow, parallel algorithms, real-time processing, Connection Machine, motion perception, optical illusions, velocity field estimation, image sequences
- MeSH terms
- Algorithms, Animals, Computer Systems, Humans, Models, Psychological, Motion Perception, Psychophysics, Robotics, Visual Cortex, Optics and Photonics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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