**Ransac Method** implemented in C++ for robust keypoints estimation in Computer Vision

Random sample consensus(RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed. The algorithm was first published by Fischler and Bolles at SRI International in 1981.

A basic assumption is that the data consists of “inliers”, i.e., data whose distribution can be explained by some set of model parameters, though may be subject to noise, and “outliers” which are data that do not fit the model. The outliers can come, e.g., from extreme values of the noise or from erroneous measurements or incorrect hypotheses about the interpretation of data. RANSAC also assumes that, given a (usually small) set of inliers, there exists a procedure which can estimate the parameters of a model that optimally explains or fits this data.WIKIPEDIA

Here the code (under GitHub-Gist) RansacMethodCPP