Stereo Matching and Optical Flow Field Computation

This MATLAB codebase provides robust implementations for calculating stereo matching and optical flow fields, designed to assist researchers and developers in computer vision applications. The implementation incorporates established algorithms such as block matching for stereo correspondence and gradient-based methods like Lucas-Kanade or Horn-Schunck for optical flow estimation. The code leverages MATLAB's powerful matrix operations and image processing toolbox to efficiently compute disparity maps and motion vectors. Key functions include image preprocessing routines, cost function computation for matching, and optimization techniques for flow field regularization. The algorithm implementation handles both dense and sparse correspondence matching with configurable parameters for window sizes, search ranges, and smoothness constraints. Throughout the code, detailed inline comments explain each computational step, including intensity normalization, gradient calculation, iterative refinement processes, and post-processing techniques for noise reduction. The implementation also includes visualization modules that generate heat maps for disparity and flow vectors, enabling intuitive analysis of the results. The architecture supports multiple input formats and includes validation checks for image pairs consistency. Users can modify algorithm parameters through structured configuration files and benefit from the modular design that separates core computation from visualization and validation routines. This comprehensive implementation serves as both a practical tool for immediate application and an educational resource for understanding fundamental computer vision techniques. Researchers can extend the base functionality by integrating additional constraints or testing alternative optimization methods while maintaining the core framework's reliability.