Harris Corner Detection Algorithm for Image Segmentation

This program implements the Harris corner detection algorithm for image segmentation. The Harris corner detection algorithm is a widely-used computer vision technique designed to identify corner points in digital images. The algorithm operates by analyzing intensity variations within local image regions, calculating gradient covariance matrices to determine corner responses based on pixel intensity differences. Corner points play a critical role in image segmentation tasks as they help localize object boundaries and structural intersections. The implementation involves key computational steps including Gaussian smoothing, gradient computation using Sobel operators, construction of the Harris matrix (autocorrelation matrix), and corner response calculation using the eigenvalues-based metric R = det(M) - k*trace(M)^2. By leveraging these detected corners for segmentation initialization, the program aims to enhance segmentation accuracy and improve boundary delineation performance in complex visual scenes.