Segmented-Based Stereo Matching Algorithm Code by International Developer

This is a segmented-based stereo matching algorithm implementation originally developed by an international programmer. The code runs directly in MATLAB 7.1 and subsequent versions without requiring additional toolboxes. The implementation follows a structured pipeline approach consisting of three main phases: image segmentation using watershed or similar algorithms, cost computation through matching cost functions, and disparity optimization via winner-takes-all or graph-cut methods. The clean architecture separates core functionalities into distinct modules, allowing easy parameter adjustment for experimental variations. Key parameters include segmentation threshold, matching window size, and disparity search range, which users can modify to observe different matching results and better understand the algorithm's behavior. This implementation serves as a valuable resource for computer vision research, particularly in applications such as 3D reconstruction, object recognition, and depth estimation. The commented code provides insights into practical implementation details while maintaining academic rigor. We hope this implementation proves beneficial for your learning and research endeavors in stereo vision algorithms!