Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this paper we present a novel combination of RANSAC plus Extended Kalman Filter (EKF) that uses the availa...

Plane detection is a prerequisite to a wide variety of vision tasks. RANdom SAmple Consensus (RANSAC) algorithm is widely used for plane detection in point cloud data. Minimum description length (MDL) principle is used to deal with several competing hypothesis. This paper presents a new approach to the plane detection by integrating RANSAC and MDL. The method could avoid detecting wrong planes ...

Abstract—In this paper, we propose a modified version of the Random Sample Consensus (RANSAC) method for Interferometric Synthetic Aperture Radar (InSAR) image registration based on the ScaleInvariant Feature Transform (SIFT). Because of speckle, the “maximization of inliers” criterion in the original RANSAC cannot obtain the optimal results. Since in InSAR image registration, the registration ...

This paper compares a new algorithm with two well-known algorithms for precise alignment of overlapping adjacent images. The new part of the algorithm is concerned with the selection of exactly matching pairs of feature points in the two images and its performance is compared with the performance of the Least Median of Square regression algorithm (LMedS), and the Random Sample Consensus (RANSAC...

Training Support Vector Machines (SVMs) to predict drugs concentrations is often difficult because of the high level of noise in the training data, due to various kinds of measurement errors. We apply RANdom SAmple Consensus (RANSAC) algorithm in this paper to solve this problem, enhancing the prediction accuracy by more than 40% in our particular case study. A personalized sample selection met...

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which ...

For the purpose of meeting the requirement for image chromatic information storage, data processing and transmission in turbulence precise detection, this paper presents a new data optimization method of turbulence image chromatic data based on energy optimization surface construction and multi-order Random Sample Consensus (RANSAC) estimation. Though extracting turbulence image’s chromatic dat...

The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner...

A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/ smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper d...

The RANdom SAmple Consensus (RANSAC) algorithm proposed by Fischler and Bolles [1] is a general parameter estimation approach designed to cope with a large proportion of outliers in the input data. Unlike many of the common robust estimation techniques such as M-estimators and least-median squares that have been adopted by the computer vision community from the statistics literature, RANSAC was...