Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant proportion of outliers. The RANSAC algorithm is possibly the most widely used robust estimator in the field of computer vision. In the paper we show that under a broad range of conditions, RANSAC efficiency is significantly improved if its hypothesis eval...

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...

We propose a new method that uses an iterative closest point (ICP) algorithm to fit three‐ dimensional points to a prior geometric model for the purpose of determining the position and orientation (pose) of a sensor with respect to a model. We use a method similar to the Random Sample and Consensus (RANSAC) algorithm. However, where RANSAC uses random samples of points in the fitting trials, DI...

Because tunnels generally have tubular shapes, the distribution of tie points between adjacent scans is usually limited to a narrow region, which makes the problem of registration error accumulation inevitable. In this paper, a global registration method is proposed based on an augmented extended Kalman filter and a central-axis constraint. The point cloud registration is regarded as a stochast...

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensin...

Scene analysis is a prior stage in many computer vision and robotics applications. Thanks to recent depth camera, we propose a fast plane segmentation approach for obstacle detection in indoor environments. The proposed method Fast RANdom Sample Consensus (FRANSAC) involves three steps: data input, data preprocessing and 3D RANSAC. Firstly, range data, obtained from 3D camera, is converted into...

Robust statistical methods were first adopted in computer vision to improve the performance of feature extraction algorithms at the bottom level of the vision hierarchy. These methods tolerate the presence of data points that do not obey the assumed model such points are typically called “outlier”. Recently, various robust statistical methods have been developed and applied to computer vision t...

In this work an approach for image based 6-DOF pose estimation, with respect to a given 3D point cloud model, is presented. We use 3D annotated training views of the model from which we extract natural 2D features, which can be matched to the query image 2D features. In the next step typically the Perspective-N-Point Problem in combination with the popular RANSAC algorithm on the given 2D-3D po...