Acquiring qualified samples for RANSAC using geometrical constraints

Le, Van-Hung; Vu, Hai; Nguyen, Thi-Thuy; Le, Thi‐Lan; Tran, Thanh-Hai

doi:10.1016/j.patrec.2017.12.012

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…It is important to note that the behavior of the curve fit by the RANSAC algorithm follows almost the same trend as the QR. However, it was fit according to an optimal configuration, which randomly selects the least amount of observations necessary to estimate the model parameters (DERPANIS et al, 2010), removing influential data and keeping the model estimates stable against noise (LE et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Another option with robust estimators is the Random sample consensus algorithm (RANSAC) and its variations, which was proposed to obtain estimates of model parameters by removing outliers and keeping the model estimates stable against noise (LE et al, 2017). It is an algorithm which generates candidate solutions using the minimum amount of observations necessary to estimate the parameters of a given predefined model, unlike conventional methods which use the largest amount of data possible to obtain the initial solution.…”

Section: Introductionmentioning

confidence: 99%

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Use of Quantile Regression and Ransac Algorithm in Fitting Volume Equations Under the Influence of Discrepant Data

Abreu

Soares

Leite

et al. 2021

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The objective of this study was to evaluate three estimation methods to fit volume equations in the presence of influential or leverage data. To do so, data from the forest inventory carried out by the Centro Tecnológico de Minas Gerais Foundation were used to fit the Schumacher and Hall (1933) model in its nonlinear form for Cerradão forest, considering the quantile regression (QR), the RANSAC algorithm and the nonlinear Ordinary Least Squares (OLS) method. The correlation coefficient ( ) between the observed and estimated volumes, root-mean-square error (RMSE), as well as graphical analysis of the dispersion and distribution of the residuals were used as criteria to evaluate the performance of the methods. After the analysis, the nonlinear least squares method presented a slightly better result in terms of the goodness-of-fit statistics, however it altered the expected trend of the fitted curve due to the presence of influential data, which did not happen with the QR and the RANSAC algorithm, as these were more robust in the presence of discrepant data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Quantile Regression and Ransac Algorithm in Fitting Volume Equations Under the Influence of Discrepant Data

Abreu

Soares

Leite

et al. 2021

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“…Rule-based classification takes handcrafted features as geometric constraints and statistical rules [12][13][14][15][16][17]. Vosselman et al [13] extract parameterized shapes (i.e., planes, spheres, cylinders) from the laser points using 3D Hough transform.…”

Section: Point Cloud Classificationmentioning

confidence: 99%

An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020

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Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

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“…They used more criteria to find the transformation between images. Similarly, Le et al [9]. proposed an efficient sampling technique using shape prior information for fitting a cylindrical object from a 3D point cloud using RANSAC.…”

Section: Introductionmentioning

confidence: 99%

“…proposed an efficient sampling technique using shape prior information for fitting a cylindrical object from a 3D point cloud using RANSAC. Both studies in [8] and [9] provide a base for our work in this paper. The main disadvantages of all these aforementioned methods are iterative and probabilistic.…”

Section: Introductionmentioning

confidence: 99%

Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

Elibol

Chong

2019

JMSE

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Image registration is one of the most fundamental and widely used tools in optical mapping applications. It is mostly achieved by extracting and matching salient points (features) described by vectors (feature descriptors) from images. While matching the descriptors, mismatches (outliers) do appear. Probabilistic methods are then applied to remove outliers and to find the transformation (motion) between images. These methods work in an iterative manner. In this paper, an efficient way of integrating geometric invariants into feature-based image registration is presented aiming at improving the performance of image registration in terms of both computational time and accuracy. To do so, geometrical properties that are invariant to coordinate transforms are studied. This would be beneficial to all methods that use image registration as an intermediate step. Experimental results are presented using both semi-synthetically generated data and real image pairs from underwater environments.

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Acquiring qualified samples for RANSAC using geometrical constraints

Cited by 21 publications

References 5 publications

Use of Quantile Regression and Ransac Algorithm in Fitting Volume Equations Under the Influence of Discrepant Data

Use of Quantile Regression and Ransac Algorithm in Fitting Volume Equations Under the Influence of Discrepant Data

An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

Efficient Image Registration for Underwater Optical Mapping Using Geometric Invariants

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