Harris Corner Detection Algorithm

The Harris corner detection algorithm is a fundamental method in image processing and computer vision used for identifying corner points in digital images. This algorithm operates by analyzing intensity variations around each pixel to detect locations where significant changes occur in multiple directions - characteristic of corner points. The implementation typically involves calculating the structure tensor matrix for each pixel using image gradients, then computing the corner response function R = det(M) - k(trace(M))^2, where M represents the structure tensor and k is an empirical constant (usually between 0.04-0.06). Key implementation steps include: 1) Computing x and y derivatives using Sobel or similar convolution kernels, 2) Generating products of derivatives (Ix^2, Iy^2, IxIy), 3) Applying Gaussian smoothing to these products, 4) Calculating the corner response function for each pixel, and 5) Applying non-maximum suppression to identify prominent corners. These detected feature points serve as critical anchors for various applications including object tracking, image matching, stereo vision, and 3D reconstruction, providing stable reference points that are invariant to rotation and partially invariant to illumination changes.