CoreSource checked

ORB

Detects oriented FAST keypoints and describes them with rotated binary BRIEF-style tests.

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Quick Facts

Compact metadata for implementation and discovery.

Category: Image Processing
Difficulty: Intermediate
Family: Feature Detection
Tier: Core
Status: Verified
Review: Source Checked
Sources: 2
Formulas: 0
Relationships: 7
Code example: Ready
Visualization: Ready
Aliases: Oriented FAST and Rotated BRIEF

Overview

ORB combines fast corner detection with an orientation estimate and a binary descriptor. It is designed as a fast, rotation-aware alternative to heavier floating-point descriptors.

Core Concepts

fast binary feature detectionfeature detectionfeature matchingimage processing

Quick facts

Complexity

Runtime and memory behavior at a glance.

Best

O(n)

Average

O(n log n)

Worst

O(n^2)

Space

O(n)

When to Use

Use when speed and compact binary descriptors matter.
Use when moderate rotation robustness is needed but full SIFT-style descriptors are too costly.

When Not to Use

Avoid when descriptor distinctiveness matters more than speed.
Avoid in low-texture scenes where corner detection is unreliable.

What It Does

Detects candidate corners quickly
Assigns an orientation to each keypoint
Builds a binary descriptor from intensity comparisons

Use Cases

Real-time feature matching
Mobile or embedded visual tracking
Fast image registration pipelines

Inputs

2D image intensity array I(x, y)
Scale-space octave parameters and contrast/corner thresholds

Outputs

Scale- and rotation-invariant keypoints (x, y, σ, θ)
Local descriptor vectors (floating-point or binary)

Pseudocode

1.Detect FAST-style keypoints across image scales
2.Rank or filter keypoints by corner response
3.Estimate orientation from local intensity moments
4.Rotate the binary comparison pattern
5.Compute the binary descriptor and match with Hamming distance

Canonical Example

Input: two rotated views of a textured label
Output: binary descriptor matches for corresponding corners
Explanation: ORB orients each keypoint and compares binary descriptors with Hamming distance.

Implementation Notes

Use Hamming distance for binary descriptor matching.
Limit keypoints per scale level to keep matching predictable.
Pair matches with geometric verification before trusting correspondences.

Common Pitfalls

Comparing ORB descriptors with Euclidean distance instead of Hamming distance is wrong.
Skipping orientation handling makes matches rotation-sensitive.
Too many weak keypoints can overwhelm matching and RANSAC verification.