Feature-Based Alignment

Feature-based alignment is the pipeline that turns two (or more) images of the same scene into a set of pixel-to-pixel correspondences, and then uses those correspondences to estimate a geometric transformation between the images. It is the workhorse of classical computer vision: everything from image mosaicing to epipolar geometry to structure from motion rests on this three-stage recipe.

The pipeline decomposes into three stages, each with its own set of design choices:

1. Feature Detection — find repeatable, well-localized keypoints in each image.
2. Feature Description — summarize each keypoint’s neighborhood as a matchable descriptor vector.
3. Feature Matching — pair descriptors across images, filter ambiguous pairs, and verify geometric consistency with RANSAC.

Subfolders

• 4.1. Feature Detection — where keypoints come from
• 4.2. Feature Selection — how keypoints are culled before description
• 4.3. Feature Description — how patches turn into vectors
• 4.4. Feature Matching — how vectors turn into correspondences

See Also

• 3.4. Homographies — the planar alignment model estimated from matches
• 5.3. Epipolar Geometry — the two-view geometric model recovered from matches
• SfM — the multi-view pipeline built on feature-based alignment