Harris Corner Detection Code Implementation

In the field of computer vision, the Harris corner detection algorithm is widely used. Proposed in 1988 by Chris Harris and Mike Stephens, this algorithm detects corners in images. Corners, as the name suggests, refer to intersections of lines, but in practical applications, the concept extends to locations where pixel values change most dramatically. Corner detection algorithms can be classified into three categories: gray-scale image-based, binary image-based, and contour curve-based detection. Gray-scale based methods can be further subdivided into gradient-based, template-based, and hybrid template-gradient approaches. In template-based methods, the primary focus is on intensity variations within pixel neighborhoods, essentially changes in image brightness. A pixel is defined as a corner if it shows sufficient brightness contrast with its neighboring pixels. The Harris algorithm employs a template-based approach that involves several computational steps: The implementation typically begins with calculating image gradients using Sobel or similar operators to obtain Ix and Iy derivatives. These gradients are used to compute the structure tensor components (Ix², Iy², IxIy) followed by Gaussian filtering to create the smoothed tensor matrix. The corner response function R = det(M) - k(trace(M))² is then calculated for each pixel, where M represents the structure tensor and k is an empirical constant (usually 0.04-0.06). Key implementation aspects include: - Gradient computation using convolution with derivative kernels - Gaussian smoothing of the structure tensor components - Eigenvalue analysis through the corner response function - Non-maximum suppression to identify local maxima - Thresholding to select significant corners The algorithm effectively identifies corners by locating pixels with high response values, making it a robust method for feature detection in computer vision applications. Its implementation requires careful parameter tuning for optimal performance across different image types and scenarios.