RANSAC as a Robust Method in Computer Vision

In the field of computer vision, RANSAC serves as a commonly employed robust estimation method, particularly useful for registering two images. RANSAC stands for "Random Sample Consensus." The algorithm operates by randomly selecting subsets of data to estimate model parameters and subsequently computing the error between data points and the estimated model. Using predefined thresholds, it identifies "inliers"—data points consistent with the model—which are then utilized to refine the parameter estimation. This iterative process continues until specific convergence criteria are met. Consequently, the RANSAC algorithm finds applications not only in image processing but also in numerous other domains such as machine learning and computer graphics. From an implementation perspective, a typical RANSAC workflow involves looping through random sampling, model fitting using minimal data points (e.g., homography estimation requires 4 correspondences), inlier counting based on a distance threshold, and retaining the model with the highest inlier count after iterations.