MATLAB Implementation of Harris Corner Detection with Code Explanation

Harris corner detection is a fundamental method in computer vision for identifying corner points within digital images. This technique enables reliable matching between two images by locating their common feature points. The algorithm operates by analyzing intensity variations around each pixel, specifically detecting points where significant intensity changes occur in multiple directions - these are classified as corners. In MATLAB implementation, this typically involves calculating the image gradient using built-in functions like 'imgradientxy', constructing the structure tensor matrix for each pixel, computing the corner response function R = det(M) - k*trace(M)^2 (where M is the structure tensor and k is an empirical constant usually between 0.04-0.06), and finally applying non-maximum suppression to identify prominent corners. Through this process, we can extract distinctive feature points that remain invariant to rotation and illumination changes, making them ideal for applications such as image matching, stereo vision, and object recognition. The Harris corner detector's robustness and computational efficiency have established it as a cornerstone technique in computer vision, with extensive applications in image processing, pattern recognition, and 3D reconstruction.