Eight-Point Algorithm in Epipolar Geometry: MATLAB Implementation Approach

I have initiated research on implementing the eight-point algorithm for epipolar geometry using MATLAB. Through literature review and practical experimentation, I have progressively grasped the algorithm's theoretical foundations and applications. During implementation, I encountered several challenges that were resolved through systematic debugging and optimization processes. The implementation utilizes MATLAB's programming environment, where I developed functions for keypoint detection using feature extraction methods like SIFT or SURF. These detected features serve as inputs for epipolar geometry calculations, specifically for computing the fundamental matrix using the eight-point algorithm. To enhance precision, I incorporated image processing techniques such as histogram equalization and Gaussian filtering to improve algorithmic robustness. A critical observation during implementation revealed the algorithm's sensitivity to input image quality. To address this, I dedicated substantial effort to preprocessing pipelines including noise reduction using median filtering, contrast enhancement through adaptive histogram equalization, and distortion correction via camera calibration parameters. The code structure implements iterative refinement loops to optimize feature matching accuracy. Furthermore, I investigated the algorithm's applicability across diverse scenarios by testing on heterogeneous image datasets containing indoor/outdoor environments and varying illumination conditions. The implementation incorporates adaptive thresholding mechanisms where the RANSAC algorithm filters outlier correspondences. While satisfactory results were achieved in most cases, edge cases require parameter tuning in the fundamental matrix computation or implementation of normalized eight-point algorithm variants. Overall, this MATLAB implementation of the eight-point algorithm presents both challenges and intellectual rewards. The project involved creating modular functions for feature matching (using matchFeatures), fundamental matrix estimation (estimateFundamentalMatrix), and epipolar line visualization. Through iterative optimization cycles and precision metrics evaluation, I have developed proficiency in stereo vision implementation techniques that will significantly contribute to my academic and professional growth in computer vision applications.