Classic Optical Flow (Motion Estimation) Algorithm: Horn and Schunck Algorithm with MATLAB Implementation

This document discusses the classic optical flow algorithm – the Horn and Schunck method, along with its corresponding MATLAB implementation. The algorithm operates by minimizing an energy function that combines brightness constancy constraints with spatial smoothness regularization, typically solved through iterative numerical methods. Beyond the basic implementation, we can explore in detail the algorithm's advantages (such as producing dense flow fields) and limitations (including sensitivity to lighting changes and slow convergence). Comparative analysis with other optical flow methods like Lucas-Kanade (local approach) or modern deep learning-based techniques would provide valuable insights. Additionally, we can introduce recent optical flow algorithms and examine their performance characteristics and practical limitations in real-world applications. Finally, we can discuss optical flow applications in computer vision domains such as object tracking, motion analysis, and video stabilization, along with current research trends including real-time processing, occlusion handling, and unsupervised learning approaches.