This project explores stitching images through homographies and projective warping.
This part generates image mosaics by registering, projective warping, resampling, and compositing photographs.
To find the transformation parameters between a pair of images, we can solve a linear system of equations from our correspondence points.
Panoramic pictures depend on homographies to stitch images together. Through this project, I learned that computing a homography can be simply done through a linear system of equations.
To identify interesting points to use in a homography, we can use Harris Interest Point Detector on these course provided images.
A homography only requires 4 points, so we can use Adaptive Non-Maximal Suppression to select 500 corners with the minimum suppression radius.
To extract a feature descriptor, we can extract an 8x8 patch sampled from a 40x40 window and flatten the ANMS points into a one dimensional array.
To match these feature descriptors, we can identify the most similar points with harris.dst2 and apply Lowe of thresholding obtain the nearest neighbors.
To generate a homography, we can use RANSAC to randomly select 4 points and identify the set of points that will create the most inliers.
This project taught me how to automatically identify points to use for a homography and generate incredible mosaic images.