The RANSAC algorithm (RANdom SAmple Consensus) is a robust method to estimate parameters of a model fitting the data, in presence of outliers among the data. Its random nature is due only to complexity considerations. It iteratively extracts a random sample out of all data, of minimal size sufficient to estimate the parameters. At each such trial, the number of inliers (data that fits the model within an acceptable error threshold) is counted. In the end, the set of parameters maximizing the number of inliers is accepted. The variant proposed by Moisan and Stival consists in introducing an a contrario criterion to avoid the hard thresholds for inlier/outlier discrimination. It has three consequences: The threshold for inlier/outlier discrimination is adaptive, it does not need to be fixed. It gives a decision on the adequacy of the final model: it does not provide a wrong set of parameters if it does not have enough confidence. The procedure to draw a new sample can be amended as soon as one set of parameters is deemed meaningful: the new sample can be drawn among the inliers of this model. In this particular instantiation, we apply it to the estimation of the homography registering two images of the same scene. The homography is an 8-parameter model arising in two situations when using a pinhole camera: the scene is planar (a painting, a facade, etc.) or the viewpoint location is fixed (pure rotation around the optical center). When the homography is found, it is used to stitch the images in the coordinate frame of the second image and build a panorama. The point correspondences between images are computed by the SIFT algorithm.