Implementation of Harris Corner Detector Algorithm

This implementation uses MATLAB code to realize the Harris Corner Detector algorithm, which is designed to detect feature points in digital images. The algorithm operates by analyzing intensity variations and local image structure to identify corner points. The implementation follows these key computational steps: First, compute image gradients using derivative filters (typically Sobel or Prewitt operators) to obtain Ix and Iy gradient components. Then, construct the structure tensor (also called second-moment matrix) for each pixel by calculating the products Ix², Iy², and Ix·Iy within a local window, usually applying Gaussian smoothing for noise reduction. Next, compute the corner response function R = det(M) - k·trace(M)² for each pixel, where M is the structure tensor and k is an empirical constant (typically 0.04-0.06). The algorithm then applies non-maximum suppression to identify local maxima in the response map, ensuring only the strongest corner candidates within a neighborhood are selected. Finally, appropriate thresholding is applied to the suppressed response values to determine the final set of feature points. The Harris Corner Detector effectively identifies points with significant intensity variations in multiple directions, providing fundamental support for various image processing and computer vision applications such as image matching, object recognition, and 3D reconstruction.