Robust Homography Estimation for Putative Image Point Correspondences

This text discusses robust homography estimation from a set of putative image point correspondences, a fundamental problem in computer vision with applications spanning facial recognition, object tracking, and 3D reconstruction. Homography estimation involves complex mathematical computations requiring specialized techniques to ensure optimal results. We introduce widely-used algorithms including RANSAC (Random Sample Consensus) for outlier rejection and least-squares minimization for precision fitting. The RANSAC implementation typically involves iteratively selecting minimal point sets (4 correspondences for homography), computing candidate homographies using Direct Linear Transform (DLT), and evaluating consensus through reprojection error thresholds. For inlier refinement, normalized DLT or Levenberg-Marquardt optimization minimizes the symmetric transfer error. We also address strategies for handling noise and outliers, such as incorporating PROSAC sampling or M-estimators for improved robustness. Understanding these concepts enables deeper insight into homography estimation challenges and facilitates more effective implementation in practical computer vision systems.