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opencv-wasm

BRISK features

Algorithm atlasFeatures

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

Try it on your image ↓

VISUAL WALKTHROUGHFeatures
REFERENCE INPUTTexture and corners

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

RESULTA descriptor for one patch

Patch comparisons turn local appearance into a compact signature. This grid illustrates a binary descriptor, not the complete native output.

STEP 03 / 03

Compare short-distance pairs to construct descriptor bits.

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

Try it on an image

YOUR IMAGE · REAL OPENCV

Experiment at pixel level

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Detect and draw BRISK keypoints.

The engine loads on your first run. Your images stay in this browser.

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
Hover to inspect. Click to pin a pixel.
Input 
Select a pixel

Output 
Select a pixel

Sample models and licenses

When to use it

Obtain scale- and rotation-aware binary features for fast matching.

How it works

  1. 01Detect candidate keypoints across scales.
  2. 02Use long-distance sample pairs to estimate orientation.
  3. 03Compare short-distance pairs to construct descriptor bits.

ring samples → orientation → binary comparisons

What to tune

thresh controls detection; octaves control scale coverage; patternScale changes descriptor sampling support.

Where it breaks down

Small blurred patches and repeated textures can make binary comparisons unreliable.

TypeScript API

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

xfeatures2d_BRISKReference →

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

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.

FAST corners

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