Harris Corner Detection and Image Registration Implementation for Two Images

This program delivers a robust and efficient solution by implementing Harris corner detection and registration across two images. The implementation utilizes the Harris corner detection algorithm which computes the second moment matrix (autocorrelation matrix) using image gradients, then calculates the corner response function R = det(M) - k(trace(M))² to identify distinctive corner points. For image registration, the program employs feature matching techniques such as normalized cross-correlation or RANSAC (Random Sample Consensus) to establish correspondences between detected corners in both images. This functionality enables users to accurately detect corner features and perform precise matching across different images, which is crucial for computer vision research and practical applications. Through this implementation, users can gain deeper insights into image analysis, leading to more accurate and comprehensive conclusions. The code structure includes key functions for gradient computation, corner response calculation, non-maximum suppression, and feature matching algorithms. We hope this tool enhances productivity and facilitates innovation in image processing workflows.