Direct Linear Transform
[SVD](https://texonom.com/svd-c7cdeb1b6b4747c8825b7e23c325d1ab) [Image Homography](https://texonom.com/image-homography-b4d139e73691419cad6276166b701b09) 


## DLT
**only works when the transform function is linear, doesn’t deal well with outliers.**
match feature points
노이즈가 많은 이미지에서 정확도가 떨어질 수
![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F07a48ff4-73b7-49e2-882b-a46acf744e3c%2FUntitled.png?table=block&id=da48f701-f1b9-4a2e-b0b3-df268cdd75b7&cache=v2)
### Solving
$Ah = 0$
Given $\{x_i, x_i'\}$, solve for H such that $x' = Hx$
1. For each correspondence, create 2x9 matrix $A_i$
2. Concatenate into single 2n x 9 matrix $A$
3. Compute SVD of $A = U\Sigma V^T$
4. Store singular vector of the smallest singular value $h = v_i$
5. Reshape to get $H$

### [RANSAC](https://texonom.com/ransac-3b62bfa306a24f8cb26fe4c89f8992ca) loop
make good to outliers
6. Get four points correspondences (randomly)
7. Compute H using DLT
8. Count inliers
9. Keep H if largest number of inliers








