MATLAB Implementation of Harris Corner Detection with Image Stitching

In this experiment, we will implement the Harris corner detection algorithm to extract feature points from images. The Harris corner detector works by computing the gradient covariance matrix (structure tensor) for each pixel and analyzing its eigenvalues to identify corner points where intensity changes significantly in multiple directions. We will then implement feature point matching between two images using techniques such as normalized cross-correlation or SSD (Sum of Squared Differences) to find correspondences. This matching process will enable us to stitch the images together by estimating a homography transformation matrix using RANSAC (Random Sample Consensus) to handle outliers. Through this process, we will achieve the final goal of image stitching, which involves blending the transformed images to create a seamless panoramic result. This experiment will provide deeper understanding of feature detection and matching algorithms in image processing, as well as practical skills in combining multiple images into a larger composite image using MATLAB's image processing toolbox functions.