Harris Corner Detection

In the field of computer vision, the Harris corner detection algorithm is a widely used feature point detection method. It identifies corners within images, which serve as critical feature points applicable to image registration, 3D reconstruction, and various other applications. The core principle of the Harris algorithm relies on the mathematical definition of corners, determining whether a pixel constitutes a corner by calculating intensity variations within its surrounding region. Key implementation steps involve: computing image gradients (Ix, Iy) using Sobel operators, constructing the structure tensor matrix for each pixel, calculating the corner response function R = det(M) - k*trace(M)^2 (where k is an empirical parameter typically 0.04-0.06), and applying non-maximum suppression to localize corner positions. Consequently, the Harris algorithm automatically detects numerous corners in images, providing a robust foundation for subsequent image processing tasks.