MATLAB Implementation of Harris Corner Detection Algorithm

This article presents a MATLAB implementation of the Harris corner detection algorithm. Harris corner detection is a fundamental computer vision technique for identifying distinctive keypoints in images. Originally proposed by Chris Harris and Mike Stephens in 1988, this algorithm remains widely used in modern image processing applications. The implementation involves calculating image gradients, constructing the structure tensor matrix, computing corner response functions, and applying non-maximum suppression to localize keypoints. Through Harris corner detection, we can effectively identify important features such as corners and edges in images, which proves invaluable for tasks like image matching, object recognition, and image registration. This article provides a detailed explanation of the algorithm's mathematical foundation and operational steps, accompanied by comprehensive MATLAB code examples demonstrating gradient computation using Sobel operators, structure tensor formation with window functions, corner response calculation using the Harris measure R = det(M) - k*trace(M)^2, and threshold-based keypoint selection. The implementation includes parameter optimization techniques for different image types and practical considerations for real-world applications.