RANSAC Algorithm and its Application in Mismatch Elimination

The RANSAC (Random Sample Consensus) algorithm is widely used in image registration applications, effectively eliminating mismatches through robust parameter estimation. The algorithm operates by iteratively selecting random subsets of data points, fitting models to these samples, and evaluating consensus among the remaining points. In image registration contexts, RANSAC identifies reliable correspondences between feature points by distinguishing inliers from outliers, enabling accurate alignment of image pairs. A typical implementation involves key functions like random sampling, model fitting (often using homography or affine transformations), and consensus evaluation through distance thresholds. Below demonstrates a practical RANSAC-based registration example highlighting core algorithmic components: random sample selection for initial transformation estimation, iterative inlier/outlier classification using reprojection error thresholds, and final model refinement using all identified inliers.