Abstract
We propose a robust method for detecting local planar regions in a scene with an uncalibrated stereo. Our method is based on random sampling using distributions of feature point locations. For doing the random sampling in RANSAC procedure, we use an uniform distribution and the distributions for each feature point defined by the distances between the point and the other points. We first choose a correspondence by using an uniform distribution and next choose candidate correspondences by using the distribution of the chosen point. Then, we compute a homography from the chosen correspondences and find the largest consensus set of the homography for detecting the local planar region in the scene. We repeat this procedure until all regions are detected. We demonstrate that our method is robust to the outliers in the scene by simulations and real image examples.