Performance Evaluation of RANSAC Family

Choi, Sunglok; Kim, Taemin; Yu, Wonpil

doi:10.5244/c.23.81

Cited by 334 publications

(218 citation statements)

References 28 publications

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“…There are two reasons for this failure: (1) It In reconstruction process, we have to analyze the RANSAC iteration parameters and thresholds. Guided sampling in the RANSAC process can substitute random sampling to accelerate convergence and reduces the necessary number of iteration [30]. Based on the similarity detection, the candidate samples on a single power-line span are firstly detected.…”

Section: Power-lines Reconstructionmentioning

confidence: 99%

“…After determining an initial hypothesis of the mathematical model, the other samples are gradually added into the model to determine whether they are inliers or outliers. At last, the final results are determined if their number of inliers selected by RANSAC is bigger than a voting threshold [29,30]. Using the RANSAC rule in power-line reconstruction can avoid failures of initial parameters fitting which caused by the sparseness of data.…”

Section: D Reconstruction Of Power-line Spansmentioning

confidence: 99%

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An Improved Method for Power-Line Reconstruction from Point Cloud Data

et al. 2016

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This paper presents a robust algorithm to reconstruct power-lines using ALS technology. Point cloud data are automatically classified into five target classes before reconstruction. In order to improve upon the defaults of only using the local shape properties of a single power-line span in traditional methods, the distribution properties of power-line group between two neighbor pylons and contextual information of related pylon objects are used to improve the reconstruction results. First, the distribution properties of power-line sets are detected using a similarity detection method. Based on the probability of neighbor points belonging to the same span, a RANSAC rule based algorithm is then introduced to reconstruct power-lines through two important advancements: reliable initial parameters fitting and efficient candidate sample detection. Our experiments indicate that the proposed method is effective for reconstruction of power-lines from complex scenarios.

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Section: Power-lines Reconstructionmentioning

confidence: 99%

Section: D Reconstruction Of Power-line Spansmentioning

confidence: 99%

An Improved Method for Power-Line Reconstruction from Point Cloud Data

et al. 2016

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“…In this study, we used an optimized version of the Classification and Regression Three algorithm. RANSAC is an iterative method used to estimate parameters of a mathematical model from a set of observed data, in which the maximum residual for a data sample is classified as being an inlier (Alive) if it is lower than the residual threshold, otherwise the sample is classified as being an outlier (Death) [21]. The Random Forest classifier is a powerful machine learning classifier, which has the key advantages of a having nonparametric nature, high classification accuracy, and the capability to determine variable importance [22].…”

Section: Feature Selection and Machine Learningmentioning

confidence: 99%

Development of 365-day Life Expectancy Models: Application of Machine Learning Methods to a Prospective Study of Critically Ill Cirrhotic Patients (Preprint)

Hu¹,

Pan²,

Tsai³

et al. 2018

Preprint

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Background: The mortality rate of cirrhotic patients in intensive care units (ICUs) is usually high. Analyzing high dimensional data and incorporating accurate life expectancy indices using machine learning methods may improve the accuracy of the prediction of the long-term prognosis of critically ill cirrhotic patients. Objective: To develop highly accurate 365-day candidate life expectancy models based on machine learning methods, and compare their accuracy with CLIF-SOFA scores in critically ill cirrhotic patients.

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“…If the number of outliers is greater than 40-45%, these methods do not generate the correct results. Except from strictly statistical methods, there is a group of numerical methods, which deal with outliers (ISACK and BOYKOV, 2012;CHOI et. al., 2009).…”

Section: Introductionmentioning

confidence: 99%

Outliers Detection by Ransac Algorithm in the Transformation of 2d Coordinate Frames

Janicka¹,

Rapiński²

2014

Bol. Ciênc. Geod.

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Over the years there have been a number of different computational methods that allow for the identification of outliers. Methods for robust estimation are known in the set of M-estimates methods (derived from the method of Maximum Likelihood Estimation) or in the set of R-estimation methods (robust estimation based on the application of some rank test). There are also algorithms that are not classified in any of these groups but these methods are also resistant to gross errors, for example, in M-split estimation. Another proposal, which can be used to detect outliers in the process of transformation of coordinates, where the coordinates of some points may be affected by gross errors, can be a method called RANSAC algorithm (Random Sample and Consensus). The authors present a study that was performed in the process of 2D transformation parameter estimation using RANSAC algorithm to detect points that have coordinates with outliers. The calculations were performed in three scenarios on the real geodetic network. Selected coordinates were burdened with simulated values of errors to confirm the efficiency of the proposed method. Keywords: Coordinate Transformation; RANSAC; Parameter Estimation. RESUMO

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Performance Evaluation of RANSAC Family

Cited by 334 publications

References 28 publications

An Improved Method for Power-Line Reconstruction from Point Cloud Data

An Improved Method for Power-Line Reconstruction from Point Cloud Data

Development of 365-day Life Expectancy Models: Application of Machine Learning Methods to a Prospective Study of Critically Ill Cirrhotic Patients (Preprint)

Outliers Detection by Ransac Algorithm in the Transformation of 2d Coordinate Frames

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