Harris Corner Detection with NCC Matching and RANSAC Optimization

This code implements a comprehensive feature detection and matching pipeline. It begins with Harris corner detection algorithm to identify key interest points in images by analyzing local intensity variations using an autocorrelation matrix. The implementation typically involves computing image gradients, constructing the Harris matrix, and calculating corner response scores. Following corner detection, the normalized cross-correlation (NCC) algorithm performs initial feature matching by comparing pixel intensity patterns around detected corners. NCC implementation includes window-based similarity computation with normalization for illumination invariance. Finally, the RANSAC (Random Sample Consensus) algorithm robustly estimates the geometric transformation model and filters out mismatched points through iterative sampling and consensus evaluation. This three-stage approach significantly enhances matching reliability by maintaining geometrically consistent correspondences while rejecting spatial outliers, thereby improving both matching accuracy and system stability for computer vision applications.