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opencv-wasm

ORB features

Algorithm atlasFeatures

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

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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

Rotate binary intensity comparisons to build a descriptor.

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 oriented ORB keypoints on the image.

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 
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Sample models and licenses

When to use it

Match textured objects quickly on CPU, track scenes, or build lightweight recognition pipelines.

How it works

  1. 01Find FAST corners at several pyramid levels.
  2. 02Estimate a dominant orientation for each retained patch.
  3. 03Rotate binary intensity comparisons to build a descriptor.

keypoint → oriented patch → binary descriptor

What to tune

nfeatures limits retained points; scaleFactor and nlevels cover scale; WTA_K affects the correct Hamming norm.

Where it breaks down

Large viewpoint changes and repeated or textureless surfaces remain difficult. Binary descriptors require a suitable Hamming distance.

TypeScript API

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

ORBReference →ORB_createReference →

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

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.

FAST corners

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