A Combined RANSAC-Hough Transform Algorithm for Fundamental Matrix Estimation
نویسندگان
چکیده
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.
منابع مشابه
Performance Evaluation of RANSAC Family
Random Sample Consensus (RANSAC) [3] has been popular in regression problem with samples contaminated with outliers. M-estimator, Hough transform, and others had been utilized before RANSAC. However, RANSAC does not use complex optimization as like M-estimator. It does not need huge amounts of memory as like Hough transform to keep parameter space. RANSAC is simple iteration of two steps: hypot...
متن کاملAutomatic Estimation of Epipolar Geometry from Blob Features Report LiTH-ISY-R-2620
This report describes how blob features can be used for automatic estimation of the fundamental matrix from two perspective projections of a 3D scene. Blobs are perceptually salient, homogeneous, compact image regions. They are represented by their average colour, area, centre of gravity and inertia matrix. Coarse blob correspondences are found by voting using colour and local similarity transf...
متن کاملLatent RANSAC
We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only that we need as few as two votes for a successful detection. Rapidly locatin...
متن کاملFive-point Fundamental Matrix Estimation for Uncalibrated Cameras
We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g. the SIFT detector. The proposed minimal solver first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the fundamental matrix is obtained from the homography and two additional point p...
متن کاملHough-transform and Extended Ransac Algorithms for Automatic Detection of 3d Building Roof Planes from Lidar Data
Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D Lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial...
متن کامل