Overview
Lucas-Kanade optical flow assumes nearby pixels move together and solves for the displacement that best matches brightness changes.
CoreSource checked
Lucas-Kanade Optical Flow
Estimates local image motion by solving small least-squares systems over patches.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Image Processing
- Difficulty
- Advanced
- Family
- Optical Flow
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 2
- Formulas
- 1
- Relationships
- 3
- Code example
- Ready
- Visualization
- Ready
Core Concepts
brightness constancylocal patchleast squaresimage gradient
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(pixels)
Average
O(pixels * window)
Worst
O(pixels * window)
Space
O(pixels)
When to Use
- Use when image enhancement needs brightness constancy.
- Use when the input matches the assumptions behind optical Flow.
When Not to Use
- Avoid when image noise, lighting, or scale breaks the algorithm's assumptions.
- Avoid when a simpler algorithm gives the required result with less setup.
What It Does
- Estimates local image motion by solving small least-squares systems over patches
- Uses brightness constancy and local patch to guide the work.
- Produces processed image.
Use Cases
- Image enhancement
- Feature extraction
- Segmentation and motion analysis
Inputs
- Image or image sequence
- Kernel, threshold, or model parameters
Outputs
- Processed image
- Mask
- Feature map or motion field
Key Formulas (1)
Optical flow constraint
Ix · u + Iy · v + It = 0
I_x * u + I_y * v + I_t = 0Relates image gradients to local motion components.
Pseudocode
- 1.Normalize the image and choose border handling
- 2.Evaluate the local filter, statistic, model, or response for each relevant pixel or region
- 3.Threshold, rank, refine, or combine the responses
- 4.Return the mask, transformed image, feature set, or geometric estimate
Canonical Example
- Input
- a small grayscale image patch with a clear edge, corner, or region
- Output
- Processed image
- Explanation
- Lucas-Kanade Optical Flow applies brightness constancy to produce processed image.
Implementation Notes
- Handle borders and pixel coordinate conventions consistently.
- Keep the brightness constancy step separate enough to test independently.
- Keep coordinate conventions, border handling, and image normalization explicit in tests.
Common Pitfalls
- Applying operations in the wrong color space can distort the result.
- Test edge cases, not only clean examples.
- Thresholds tuned on one clean image can fail under different lighting, scale, blur, or noise.
Interactive Mechanism Walkthrough
Spatial Primitive & Sweep-Line CanvasShow the source image beside masks, filters, or motion vectors produced by the algorithm.
Step 1 of 4: Normalize the image and choose border handling
image-processing-mechanismNotes
Lucas-Kanade Optical Flow should be used with the assumptions and tradeoffs of optical Flow in mind.
Sources (2)
What each reference supports for this entry.
Computer Vision: Algorithms and Applications
BookComputer-vision textbook support for Lucas-Kanade Optical Flow's feature, matching, flow, or multiscale image-analysis workflow.
Open sourceLucas-Kanade Image Registration
PaperOriginal reference for local least-squares image registration underlying Lucas-Kanade optical flow.
Open sourceExplore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageUsesImage Pyramid
Pyramidal Lucas-Kanade uses image pyramids to handle larger motions across scales.
Related Algorithms
Tags
Image ProcessingMotion EstimationLinear AlgebraEstimation