Image Stitching Program with Harris Corner Detection and RANSAC Matching

This image stitching program, originally discovered on an international website, efficiently combines three images into a unified panoramic image. The core implementation follows a robust computer vision pipeline: initially employing the Harris corner detection algorithm to identify distinctive feature points across images. These detected corners then undergo coarse matching to establish preliminary correspondences, followed by refinement using the RANSAC (Random Sample Consensus) algorithm to eliminate outliers and achieve precise alignment. The codebase features extensive inline comments explaining each processing stage, supplemented by a detailed documentation file that elaborates on the algorithmic approach and implementation specifics. This makes the program particularly suitable for beginners seeking to understand fundamental image stitching methodologies. Beyond the core functionality, the program supports multiple image formats including JPEG and PNG, ensuring broad compatibility with common image types. Optimized for performance, the implementation demonstrates rapid processing capabilities, completing stitching tasks efficiently even with standard hardware configurations. The technical implementation leverages key OpenCV functions such as cv2.cornerHarris() for feature detection and cv2.findHomography() for perspective transformation estimation. The RANSAC integration ensures robustness against matching errors by iteratively validating geometric consistency across candidate point pairs. Overall, this image stitching program serves as both a practical tool for panoramic image creation and an educational resource for understanding computer vision algorithms, providing substantial value for students and developers entering the field of computational photography.