Feature Detection Online Free — Harris FAST ORB

How feature detection actually works

Feature detection finds interest points — corners and high-contrast spots that stay recognizable even when an image is rotated, scaled, or slightly relit. This tool runs three classic computer-vision algorithms entirely on your device. Harris uses the second-moment matrix (structure tensor) of local image gradients to score corners, marking pixels where intensity changes sharply in two directions (a true corner) rather than along a single line (an edge). FAST (Features from Accelerated Segment Test) is the speed champion: it inspects a 16-pixel Bresenham ring around each candidate and flags it as a keypoint when a long contiguous arc is all brighter or all darker than the center, which makes it ideal for real-time work. ORB (Oriented FAST and Rotated BRIEF) runs FAST across an image pyramid for scale awareness, assigns each keypoint an orientation from its intensity centroid so descriptors survive rotation, and emits a compact binary descriptor you can use to match the same point across two different photos.

Choosing a mode and tuning it

Pick the algorithm by what you need from the result. Reach for Harris when you only want to see where the strong corners are — calibration grids, checkerboards, or architectural shots. Use FAST when raw keypoint count and speed matter and your images are shot from a similar distance, since FAST alone is not scale-invariant. Choose ORB when you intend to match features between images, because of these three it is the only one that emits scale- and rotation-aware descriptors that downstream tasks depend on. A few practical pointers:

• Quality / threshold: raise it to keep only the strongest, most repeatable points; lower it on flat or low-texture images to surface more detail.
• Max features: cap the count so dense, textured scenes don't drown the overlay in dots.
• Clean input first: grainy or blurry photos produce unstable keypoints that shift between runs, so check the underlying structure with edge detection before you trust the matches.

Where it's useful, and why on-device matters

Detected keypoints are the foundation of image registration, panorama stitching, motion tracking, object recognition, and document or logo matching. The corners ORB extracts feed directly into template matching for locating a known patch in a larger scene, and into image alignment, where matched features estimate the geometric warp that overlays two photos. Because every algorithm runs in WebAssembly right inside your browser, your photos are never uploaded to a server — there's no queue, no account, and no watermark. That privacy guarantee is the practical reason to run feature detection on-device: you can analyze sensitive scans, product mockups, or personal photos and get instant results without anything leaving your machine.