Image Feature Point Matching Implementation with Harris Corner Detection and SSD Algorithm

This program primarily implements feature point matching between two images. The workflow begins with applying the Harris corner detection algorithm to identify distinctive corner points in both images. The Harris detector works by computing the autocorrelation matrix and analyzing eigenvalue responses to locate points with significant intensity variations in both directions. Following corner detection, the program employs the Sum of Squared Differences (SSD) algorithm to filter and match corresponding corner points. The SSD matching process involves comparing small image patches around each detected corner and calculating the sum of squared intensity differences between patches. Points with minimal SSD values below a specified threshold are considered valid matches. The final stage connects the successfully matched corner points with lines, creating a visual representation of the correspondence between the two images. The implementation utilizes OpenCV's line drawing functions to create these connections between matched coordinate pairs. Additionally, the program supports various supplementary operations such as visualizing matching results through interactive displays and calculating relative motion vectors between feature points based on their matched positions. Users can optimize matching performance by adjusting algorithm parameters, including the corner response threshold in Harris detection or implementing more sophisticated feature descriptors like SIFT or ORB for improved matching accuracy. In summary, this program provides a robust and straightforward methodology for image feature point matching, serving as a fundamental tool for various computer vision applications including image registration, object recognition, and 3D reconstruction. The modular design allows for easy integration of different feature detectors and matching algorithms based on specific application requirements.