Part A
Shoot the Pictures
Recover Homographies
In this part, I computed the H matrix which has 8 degrees of freedom using 4 points labelled on both images. I computed H by solving the following system of linear equations.Stack the below 4 times for each of the 4 point correspondences:
P = [-x_i, -y_i, -1, 0, 0, 0, x_i * x'_i, y_i * x'_i, x'_i], [0,0,0,-x_i, -y_i, -1, x_i * y'_i, y_i * y'_i, y'_i] H = [h1, h2, h3, h4, h5, h6, h7, h8, h9]^T PH = 0
Image Warp
I used the H matrix calculated above to do an image warp. First, I applied H on the vertices of the source image in order to find the bounding box for the transformed image. Next, I iterated through the pixels of the destination image and colored them based on the interpolated points from the source image.
Image Rectification
For this part, I marked the points of a rectangular image and then warped the image into a square shape.
Mosaic
Using the image warped images and some blending, I was able to get a mosaic.
Part B
Harris Corners
Harris Corners were implemented by the amazing Prof. Alyosha Efros and/or amazing course staff. These points are very distinct compared to their background based on the gradient change. Some changes I made to the starter code were to use min_distance of 15 in order to reduce the number of points outputted so that anms would run in a reasonable amount of time.
Adaptive Non-maximal Suppression
Next, I used the adaptive non-maximal suppression technique (which ran very slowly before the aforementioned optimization). This part works by taking only the maximum point for a certain radius in order to ensure that there were sufficient keypoints throughout the image and not just in one noisy patch.
Feature Descriptor
I took the 40 pixels surrounding the pixel of interest, applied a gaussian blur to the region, downsampled, and normalized in order to get a 8x8x3 descriptor of the region.Feature Matching
Next, I compared the features and created pairs when 2 keypoints were the best match in terms of SSD error.
RANSAC
RANSAC goes through random sets of 4 keypoints, determines the corresponding homography & the keypoints associated with that homography, and then determines which has the max number of inliers. We then compute a homography based on the max inliers.
Auto-Moscaic Final Result
