MATLAB Implementation of Harris Corner Detection Algorithm for Image Matching

This documentation presents a MATLAB implementation of Harris corner detection algorithm for image feature matching. The Harris corner detector is a fundamental computer vision technique that identifies keypoints in images where significant intensity variations occur in multiple directions. These corner points represent distinctive features that remain stable under various transformations, making them ideal for image matching applications. The implementation calculates image gradients using Sobel or Prewitt filters, computes the second moment matrix (structure tensor) for each pixel, and derives the corner response function R = det(M) - k*trace(M)^2. Key parameters include: - Gaussian smoothing sigma for noise reduction - Sensitivity factor k (typically 0.04-0.06) - Non-maximum suppression threshold for corner selection The matching process involves: 1. Extracting corner features from both images 2. Creating feature descriptors using surrounding pixel patterns 3. Applying distance metrics (Euclidean or SSD) for descriptor matching 4. Implementing RANSAC or similar algorithms for outlier rejection The code provides visualizations of detected corners and matching results, along with performance metrics for matching accuracy. This implementation serves as both an educational tool for understanding corner detection principles and a practical foundation for computer vision applications like image stitching, object recognition, and motion tracking. The complete MATLAB script includes modular functions for gradient computation, Harris response calculation, non-maximum suppression, and feature matching, allowing easy integration into larger computer vision pipelines. Commented code sections explain each algorithmic step and provide parameter tuning guidance for different image types.