A Combined RANSAC-Hough Transform Algorithm for Fundamental Matrix Estimation

In this paper we will consider a combination of the RANSAC algorithm and the Hough transform for fast model estimation under the presence of outliers. The model will be computed by sampling a smaller than minimal subset, followed by a voting process of the remaining data. In order to use the combined method for this purpose, an adequate parameterization of the model in the Hough space is required. We will show that in case of hyperplane and fundamental matrix estimation, there is a similar and very general parameterization possible. It will allow these models to be estimated in a very efficient manner.