Keywords
Optical flowBrightnessMathematicsImage (mathematics)Flow (mathematics)AlgorithmConstraint (computer-aided design)Quantization (signal processing)Vector quantizationSequence (biology)Computer visionArtificial intelligenceComputer scienceOpticsGeometryPhysics
Affiliated Institutions
Related Publications
Determining the orientation of surfaces from optical flow
This paper presents a method for computing the orientation of physical surfaces relative to the moving observer. The method is theoretically derived; it is not 'ad hoc'. The met...
Optical Velocity Patterns, Velocity-Sensitive Neurons, and Space Perception: A Hypothesis
A hypothesis is put forward of how global patterns of optical flow, as discussed by Gibson, Johansson, and others, could be processed by relatively simple physiological mechanis...
Snakes, shapes, and gradient vector flow
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initial...
Robust Web Image/Video Super-Resolution
This paper proposes a robust single-image super-resolution method for enlarging low quality web image/video degraded by downsampling and compression. To simultaneously improve t...
The interpretation of a moving retinal image
It is shown that from a monocular view of a rigid, textured, curved surface it is possible, in principle, to determine the gradient of the surface at any point, and the motion o...
Publication Info
- Year
- 1981
- Type
- article
- Volume
- 17
- Issue
- 1-3
- Pages
- 185-203
- Citations
- 9697
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
9697
OpenAlex
Cite This
Berthold K. P. Horn,
Brian G. Schunck
(1981).
Determining optical flow.
Artificial Intelligence
, 17
(1-3)
, 185-203.
https://doi.org/10.1016/0004-3702(81)90024-2
Identifiers
- DOI
- 10.1016/0004-3702(81)90024-2