Phase-Based Optical Flow Algorithm Implementation Based on Gautama's Approach

The presented code implements the optical flow algorithm described by Gautama and Van Hulle (2002). The algorithm consists of three main computational stages. The first stage involves spatial filtering, which applies smoothing operations to reduce image noise and suppress unnecessary details through Gaussian or similar convolution filters. The second stage performs phase gradient estimation, calculating phase differences between adjacent pixels using complex-valued filters (typically Gabor or steerable filters) to obtain local phase information. The final stage employs iterative outlier cancellation (IOC) through recurrent network computations to estimate the optical flow field, where the code implements correlation-based verification and iterative refinement mechanisms. Throughout the process, the implementation includes parameter tuning and optimization procedures (such as threshold adjustments and convergence checks) to ensure algorithmic accuracy and robustness. Notably, optical flow algorithms represent a significant research direction in computer vision with broad applications including video compression, motion analysis, and object tracking. This particular implementation demonstrates one phase-based approach, while readers may select alternative algorithms (such as Lucas-Kanade or Horn-Schunck methods) or modify existing implementations to suit specific application scenarios. Furthermore, with continuous advancements in computing hardware, the computational efficiency of optical flow algorithms has significantly improved, enabling broader deployment in real-time systems and expanded application possibilities. The code structure facilitates modular adjustments to filtering parameters, phase computation methods, and IOC iterations for experimental customization.