MATLAB Implementation of Harris Corner Detection Algorithm

In this implementation, I have developed a MATLAB code for Harris corner detection. This algorithm is designed to detect corner points in images, which typically represent significant features within the image structure. Harris corner detection is a widely-used computer vision algorithm that determines corner locations by analyzing intensity variations between pixels. The implementation involves several key steps: First, the code computes image gradients using convolution with derivative filters (typically Sobel or Prewitt operators). Then, it constructs the structure tensor matrix for each pixel by calculating the products of gradients. The corner response function is implemented using the determinant and trace of the structure tensor matrix, following the formula R = det(M) - k*(trace(M))^2, where k is an empirical constant usually set between 0.04-0.06. The code includes non-maximum suppression to identify local maxima in the corner response map, ensuring accurate corner localization. Thresholding is applied to filter out weak corner responses, and the final corner coordinates are extracted for further processing. This algorithm plays a crucial role in various image processing and computer vision applications, including feature matching, object tracking, image stitching, and 3D reconstruction. Through developing this MATLAB implementation, I gained deeper understanding of the Harris corner detection principles and created a reusable codebase for corner detection tasks in personal projects. The code demonstrates proper handling of image preprocessing, parameter optimization, and result visualization aspects essential for practical computer vision applications.