number of models, nor does it necessitate manual parameters tuning. RANSAC has also been tailored for recursive state estimation applications, where the input measurements are corrupted by outliers and Kalman filter approaches, which rely on a Gaussian distribution of the measurement error, are doomed to fail. Such an approach is dubbed KALMANSAC. Related methods MLESAC (Maximum Likelihood Estimate Sample Consensus) – maximizes the likelihood that the data was generated from the sample-fitted model, e.g. a mixture model of inliers and outliers MAPSAC (Maximum A Posterior Sample Consensus) – extends MLESAC to incorporate a prior probability of the parameters to be fitted and maximizes the posterior probability KALMANSAC – causal inference of the state of a dynamical system Resampling (statistics) Hop-Diffusion Monte Carlo uses randomized sampling involve global jumps and local diffusion to choose the sample at each step of RANSAC for epipolar geometry estimation between very wide-baseline images. See also Hough transform Notes References Geometry in computer vision Statistical algorithms Statistical outliers Robust statistics Articles with example pseudocode SRI International Articles with example MATLAB/Octave code