Implementation of Eight-Point Algorithm and Optical Flow Computation with Unreliable Region Detection

This document presents two key computational modules. The first module implements the eight-point algorithm, a fundamental technique in computer vision used to estimate the fundamental matrix from corresponding image points. The algorithm operates by extracting keypoints from stereo image pairs, establishing correspondences through feature matching, and solving the linear system using normalized coordinates to ensure numerical stability. The implementation typically includes robust estimation techniques like RANSAC to handle outliers and improve accuracy in real-world scenarios. The second module focuses on optical flow computation and unreliable region detection. Optical flow represents the apparent motion of pixels between consecutive video frames, revealing both object movement and camera motion. The implementation employs differential methods (such as Lucas-Kanade or Horn-Schunck algorithms) that calculate motion vectors based on spatial and temporal intensity gradients. During this process, certain pixels exhibit unreliable motion due to factors like occlusion, textureless regions, or motion discontinuities. The module incorporates confidence measures and consistency checks to identify these unreliable areas, enabling their exclusion from subsequent processing stages through masking techniques. This ensures more robust motion analysis and improves the reliability of higher-level vision applications.