From 74ec9808dc3de08c7ad8058289923c002da28469 Mon Sep 17 00:00:00 2001 From: Bart Date: Wed, 3 May 2017 10:31:21 +0200 Subject: [PATCH] typo in docs --- doc/tutorials/content/random_sample_consensus.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/tutorials/content/random_sample_consensus.rst b/doc/tutorials/content/random_sample_consensus.rst index 0505bd45bb7..f3376b86bef 100644 --- a/doc/tutorials/content/random_sample_consensus.rst +++ b/doc/tutorials/content/random_sample_consensus.rst @@ -8,7 +8,7 @@ In this tutorial we learn how to use a RandomSampleConsensus with a plane model Theoretical Primer ------------------ -The abbreviation of "RANdom SAmple Consensus" is RANSAC, and it is an iterative method that is used to estimate parameters of a meathematical model from a set of data containing outliers. This algorithm was published by Fischler and Bolles in 1981. The RANSAC algorithm assumes that all of the data we are looking at is comprised of both inliers and outliers. Inliers can be explained by a model with a particular set of parameter values, while outliers do not fit that model in any circumstance. Another necessary assumption is that a procedure which can optimally estimate the parameters of the chosen model from the data is available. +The abbreviation of "RANdom SAmple Consensus" is RANSAC, and it is an iterative method that is used to estimate parameters of a mathematical model from a set of data containing outliers. This algorithm was published by Fischler and Bolles in 1981. The RANSAC algorithm assumes that all of the data we are looking at is comprised of both inliers and outliers. Inliers can be explained by a model with a particular set of parameter values, while outliers do not fit that model in any circumstance. Another necessary assumption is that a procedure which can optimally estimate the parameters of the chosen model from the data is available. From [Wikipedia]_: