Lucas–Kanade optical flow algorithm
E295649
The Lucas–Kanade optical flow algorithm is a widely used computer vision method for estimating the motion of features between consecutive images by assuming locally constant motion and solving a least-squares problem.
All labels observed (5)
How this entity was disambiguated
This entity first appeared as the object of triple T2739074 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
Target entity: Lucas–Kanade optical flow algorithm Context triple: [Takeo Kanade, knownFor, Lucas–Kanade optical flow algorithm]
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A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
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B.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
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C.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
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D.
Kurzweil OCR (optical character recognition) systems
Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
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E.
Kirchhoff diffraction theory
Kirchhoff diffraction theory is a classical wave optics framework that models light propagation and diffraction by treating wavefronts as superpositions of secondary spherical waves emitted from an aperture.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Lucas–Kanade optical flow algorithm Target entity description: The Lucas–Kanade optical flow algorithm is a widely used computer vision method for estimating the motion of features between consecutive images by assuming locally constant motion and solving a least-squares problem.
-
A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
-
B.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
-
C.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
D.
Kurzweil OCR (optical character recognition) systems
Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
-
E.
Kirchhoff diffraction theory
Kirchhoff diffraction theory is a classical wave optics framework that models light propagation and diffraction by treating wavefronts as superpositions of secondary spherical waves emitted from an aperture.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
computer vision algorithm
ⓘ
feature tracking method ⓘ optical flow algorithm ⓘ |
| assumption |
brightness constancy
ⓘ
locally constant motion ⓘ small motion between frames ⓘ |
| basedOn | optical flow constraint equation ⓘ |
| category | differential optical flow method ⓘ |
| commonVariant |
Lucas–Kanade optical flow algorithm
self-linksurface differs
ⓘ
surface form:
inverse compositional Lucas–Kanade
pyramidal Lucas–Kanade optical flow ⓘ |
| field |
computer vision
ⓘ
image processing ⓘ robotics ⓘ video analysis ⓘ |
| implementedIn |
MATLAB Computer Vision Toolbox
ⓘ
OpenCV ⓘ scikit-image ⓘ |
| influenced |
modern feature tracking pipelines
ⓘ
real-time visual tracking systems ⓘ |
| introducedBy |
Bruce D. Lucas
ⓘ
Takeo Kanade NERFINISHED ⓘ |
| limitation |
assumes constant motion within window
ⓘ
fails on large motions without pyramids ⓘ sensitive to illumination changes ⓘ |
| mathematicalTool |
Gauss–Newton optimization
ⓘ
normal equations ⓘ |
| operatesOn |
consecutive image frames
ⓘ
local image neighborhoods ⓘ |
| originalPublicationTitle |
Lucas–Kanade optical flow algorithm
self-linksurface differs
ⓘ
surface form:
An iterative image registration technique with an application to stereo vision
|
| originalPublicationVenue | Proceedings of Imaging Understanding Workshop ⓘ |
| output |
displacement vector of features
ⓘ
optical flow field at selected points ⓘ |
| publicationYear | 1981 ⓘ |
| relatedTo |
Horn–Schunck optical flow method
ⓘ
Kanade–Lucas–Tomasi feature tracker ⓘ
surface form:
KLT feature tracker
Lucas–Kanade optical flow algorithm self-linksurface differs ⓘ
surface form:
pyramidal Lucas–Kanade method
|
| requires | good spatial texture in local window ⓘ |
| solves | 2D motion vector for image patch ⓘ |
| typicalInput | grayscale image sequence ⓘ |
| typicalUseCase |
feature tracking in video
ⓘ
image registration ⓘ motion estimation ⓘ stereo matching refinement ⓘ visual odometry ⓘ |
| uses |
least-squares estimation
ⓘ
overdetermined linear system ⓘ spatial image gradients ⓘ temporal image gradients ⓘ |
| windowType | fixed-size image patch ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Subject: Lucas–Kanade optical flow algorithm Description of subject: The Lucas–Kanade optical flow algorithm is a widely used computer vision method for estimating the motion of features between consecutive images by assuming locally constant motion and solving a least-squares problem.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.