Image Registration Program Based on Harris Corner Feature Detection

This article explores an image registration program utilizing Harris corner features. The algorithm operates by detecting corner points in images through the Harris corner detection method, which calculates the gradient covariance matrix and evaluates corner responses using the determinant and trace. These distinctive features enable robust matching between different images. We demonstrate how to implement this approach using key functions like cornerHarris() for feature extraction and feature matching algorithms (e.g., FLANN or brute-force matchers) for correspondence establishment. The discussion covers major image registration techniques including feature-based methods (using SIFT, SURF, or ORB descriptors), area-based methods (employing intensity correlation metrics), and deep learning approaches (utilizing convolutional neural networks). We analyze each technique's advantages and limitations, providing practical guidance for selecting appropriate methods based on application requirements like computational efficiency and accuracy. Furthermore, we introduce quality assessment metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and structural similarity index (SSIM) for evaluating registration performance. The article concludes with optimization strategies including multi-scale analysis and RANSAC-based outlier rejection to enhance registration accuracy and robustness.