Source Code for Region-Based Matching in Stereo Image Matching

This implementation utilizes the stereo image matching algorithm described in the paper to perform region-based matching between left and right images, ultimately generating corresponding disparity maps. The algorithm identifies optimal matches in similar regions by comparing pixel information between the two stereo images, thereby determining object depth information. The implementation follows these key steps: First, image preprocessing techniques including noise reduction and edge detection are applied to enhance matching accuracy. The core matching process involves calculating similarity metrics (such as SAD, SSD, or NCC) between pixel blocks to find the best correspondence for each pixel in the left image within the right image. The matching algorithm typically employs sliding window techniques with customizable window sizes and search ranges. Finally, the matching results are converted into disparity maps that represent 3D shape information of objects. This region-based matching approach can be widely applied in computer vision fields including stereo vision systems, depth perception applications, and 3D reconstruction projects. The code structure includes modular functions for preprocessing, similarity computation, disparity calculation, and post-processing for optimal results.