Data Fitting Implementation Using the RANSAC Algorithm

The RANSAC (Random Sample Consensus) algorithm is a widely-used data fitting technique that plays a crucial role in applications like multi-view image matching. RANSAC operates through an iterative process where it randomly selects minimal sample subsets to generate candidate models, then evaluates model quality based on consensus set size—the number of inliers within a predefined error threshold. This iterative selection and refinement mechanism allows RANSAC to progressively optimize models while effectively handling outlier contamination. Key implementation aspects include: - Random sampling of minimal data points required for model estimation - Inlier/outlier classification using distance thresholds - Model scoring based on consensus set size - Iterative refinement until meeting convergence criteria The algorithm finds extensive applications across computer vision, machine learning, and image processing domains. Mastering RANSAC is therefore essential for robustly handling tasks involving multi-view image matching and other applications requiring outlier-resistant fitting.