Estimating a Homography model with RANSAC

Why RANSAC?

Because we want a model of good feature matches.

- Inliers:
- Good matches
- Outlier:
- Bad matches

Interest points
(500/image)
(640x480)

What other algorithms are available?

- Exhaustive search
- for each featfure, compare all other feactures in other images

- Hashing
- compute a short descriptor from each feature vector

- Nearest neighbor techniques:
- k-trees and their variants

Putative correspondences (268)

(Best match,SSD<20 span="">

Outliers (117)

(t=1.25 pixel; 43 iterations)

How about outlier rejection?

- use SSD (patch1, patch2) > threshold
- 1-NN: SSD of the closest matches

How to handle too many outliers?

RANSAC loop:

Select four feature pairs (at random)
Compute homography H (exact)
Compute inliers where SSD(pi’, H pi) < ε
Keep largest set of inliers
Re-compute least-squares H estimate on all of the inliers

Final inliers (262)

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