### image registration algorithm

Here is the image registration algorithm that I will implement; it is taken from David Capel's thesis on super resolution (pg 29):

Objective: computer the 2D homography between two images

Algorithm:

1. Features: compute interest point feature in each image to sub pixel accuracy (eg Harris corners).

2. Putative correspondences: Compute a set of interest point matches based on proximity and similarity of their intensity neighborhood.

3. RANSAC robust estimation: repeat for N samples

a. select a random sample of 4 correspondences and compute the homography H.

b. Calculate a geometric image distance error for each putative correspondence.

c. Compute the number of inliers consistent with H by the number of correspondences for which the distance error is less than a threshold.

Choose the H with the largest number of inliers.

4. Optimal estimation: re-estimate H from all correspondences classified as inliers, by maximizing the likelihood function using a suitable numerical optimizer, such as the Levenburg-Marquardt algorithm.

5. Guided matching: further interest point correspondences are now determined using the estimated H to define a search region about the transferred point position.

Objective: computer the 2D homography between two images

Algorithm:

1. Features: compute interest point feature in each image to sub pixel accuracy (eg Harris corners).

2. Putative correspondences: Compute a set of interest point matches based on proximity and similarity of their intensity neighborhood.

3. RANSAC robust estimation: repeat for N samples

a. select a random sample of 4 correspondences and compute the homography H.

b. Calculate a geometric image distance error for each putative correspondence.

c. Compute the number of inliers consistent with H by the number of correspondences for which the distance error is less than a threshold.

Choose the H with the largest number of inliers.

4. Optimal estimation: re-estimate H from all correspondences classified as inliers, by maximizing the likelihood function using a suitable numerical optimizer, such as the Levenburg-Marquardt algorithm.

5. Guided matching: further interest point correspondences are now determined using the estimated H to define a search region about the transferred point position.

## 0 Comments:

Post a Comment

<< Home