FAST corners

Algorithm atlasFeatures

Look for a contiguous arc of brighter or darker pixels around a candidate centre.

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VISUAL WALKTHROUGHFeatures

01InputFind candidate structure02OperationScore and suppress nearby candidates03ResultRetained keypoint locations

REFERENCE INPUTTexture and corners

Corners change intensity in more than one direction; a long straight edge is less distinctive.

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RESULTRetained keypoint locations

Only well-separated strong corners survive. These detector APIs do not produce descriptors.

STEP 03 / 03▷ Play steps

Optionally keep only local maxima of the corner response.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

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YOUR IMAGE · REAL OPENCV

Experiment at pixel level

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Detect FAST corners. These locations have no descriptor or scale estimate.

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Input448 × 320

OutputWaiting for a result

Scroll over either image to zoom at the pointer. Use the scrollbars to pan both views over the same relative area. Zoom is relative to the input; pixel coordinates belong to each image. Warps can change scene correspondence.

Pixel inspector RGBA · native values · matched scale · 9 × 9 output pixels

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Input

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Output

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Sample models and licenses

When to use it

Detect many corners quickly before tracking or descriptor computation.

How it works

1. 01Sample a small circle around each pixel.
2. 02Test whether a long contiguous arc differs enough from the centre.
3. 03Optionally keep only local maxima of the corner response.

corner: contiguous ring arc differs from centre by more than T

What to tune

threshold controls required contrast; nonmaxSuppression reduces clusters of adjacent detections.

Where it breaks down

FAST alone supplies no descriptor and no scale normalization. Noise affects the intensity test.

TypeScript API

Open an entry for its exact overloads, parameter descriptions, result ownership and pinned upstream source.

FastFeatureDetectorReference →

All of these calls execute on the CPU. Native objects need explicit disposal. See matrices and ownership and build compatibility.

Related methods

ORB features

Detect oriented corners across an image pyramid and describe them with compact binary tests.

SIFT features

Detect scale-space extrema and describe local gradient distributions around them.

AKAZE features

Find and describe features in a nonlinear scale space that preserves important boundaries.

BRISK features

Describe scale-space keypoints using intensity comparisons on concentric sampling rings.