A Method for Surface Reconstruction Based on Support Vector Machine

Zhang, Lianwei; Wang, Wei; Li, Yan; Liu, Xiaolin; Shi, Min; He, Hangen

doi:10.4304/jcp.4.9.806-812

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…It should be noted that support vector machines (SVM) were frequently employed for classification of objects based on features obtained from 3D point clouds [26,27]. In addition, procedures for 3D point cloud smoothing, denoising and hole filling based on ε-SVR using compactly supported Wu function kernel [28] and for surface reconstruction based on ε-SVR with Gaussian kernel [29] were proposed. However, the potentials of SVM in point cloud simplification have not been exploited yet.…”

Section: Introductionmentioning

confidence: 99%

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

2019

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Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on ε insensitive support vector regression (ε-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of ε-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines.

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Section: Introductionmentioning

confidence: 99%

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

2019

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“…Firstly proposed by Vapnik et al, Support Vector Machine (SVM) is a machine learning method which is applied to solve the binary classification problem [1][2][3]. It is an algorithm based on the VC dimension theory and the principle of structural risk minimization in the statistical learning theory,and it has the features of optimization, nuclear and the best generalization ability [4,5].The majority of the scholars have been concerned about it and it has been applied in many fields in recent years [6][7][8][9][10][11]. The majority of researchers have proposed many improved algorithms on the basis of SVM.…”

Section: Introductionmentioning

confidence: 99%

Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

Wu¹,

Ding²,

Huang³

et al. 2014

JCP

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The efficiency and performance of Twin Support Vector Machines (TWSVM) is better than the traditional support vector machines when it deals with the problems. However, it also has some problems. As the same as the traditional support vector machines, its parameters are difficult to be appointed and it is not easy to select the appropriate kernel function. TWSVM generally selects the Gaussian radial basis kernel function. Although its learning ability is very strong, its generalization ability is relatively weak. To a certain extent, this limits the performance of TWSVM .In order to solve these two problems, in this paper, we propose the Mixed Kernel Twin Support Vector Machines based on the shuffled frog leaping algorithm (SFLA-MK-TWSVM). To make full use of both the excellent generalization ability of global kernel functions and the learning ability of local kernel functions, SFLA-MK-TWSVM constructs a mixed kernel which has better performance. Then SFLA-MK-TWSVM uses the shuffled frog leaping algorithm to determine the parameters of both TWSVM and the mixed kernel function to further improve the performance of TWSVM. The experimental results indicate that SFLA-MK-TWSVM significantly improves the classification accuracy of TWSVM.Index Terms-the mixed kernel function, TWSVM, the shuffled frog leaping function

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“…It is based on the VC dimension theory and the principle of structural risk minimization in the statistical learning theory [4][5]. It has been applied in many fields [6][7][8][9][10][11] and there have been many improvements [12]. In 2001, Fung and Mangasarian [13] proposed the Proximal Support Vector Machines (PSVM).…”

Section: Introductionmentioning

confidence: 99%

Twin Support Vector Machines Based on the Mixed Kernel function

Wu¹,

Ding²

2014

JCP

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The efficiency and performance of the Twin Support Vector Machines (TWSVM) are better than the traditional support vector machines when it deals with the problems. However, it also has the problem of selecting kernel functions. Generally, TWSVM selects the Gaussian radial basis kernel function. Although it has a strong learning ability, its generalization ability is relatively weak. In a certain extent, this will limit the performance of TWSVM. In order to solve the problem of selecting kernel functions in TWSVM, we propose the twin support vector machines based on the mixed kernel function (MK-TWSVM) in this paper. To make full use of the learning ability of local kernel functions and the excellent generalization ability of global kernel functions, MK-TWSVM selects a global kernel function and a local kernel function to construct a mixed kernel function which has the better performance. The experimental results indicate that the mixed kernel function makes TWSVM have the good learning ability and generalization ability. So it improves the performance of TWSVM.

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A Method for Surface Reconstruction Based on Support Vector Machine

Cited by 5 publications

References 13 publications

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm

Twin Support Vector Machines Based on the Mixed Kernel function

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