CS 194-26 Project 5B - Andrew Loeza


In the first part of this project, we learned how to compute homographies, applied the perspective transformation to images, displayed image rectification, and then created mosaics of images. Following this, we then utilized a Harris Corner detector to detect the local maximum corners in 3 images. Then, we used Adaptive Non-Maximal Suppression to remove weak corners points while still keeping them spread out throughout the image. A Feature Descriptor Extractor was then used to generalize each point in a way that allows us to match the correct corresponding points between the images. We then use these feature descriptions to match the correct points by using Lowe's 2NN thresholding. Lastly, we then remove any incorrectly matched correspondences by using RANSAC. The correspondences that are returned by RANSAC are then used to compute the appropriate homography via least squares. Then the image is aligned and blended together using a Laplacian Pyramid.

Part A:

Shoot the Pictures:

Below are the images that I used for the entire project.

Perspective Transformation Images:

Image Rectification Images: