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基于径向基函数的残缺点云数据修复   总被引:13,自引:0,他引:13  
提出一种具有较强鲁棒性的残缺点云数据修复算法,借助kD tree寻找点云的缺陷边界,确定点云的缺陷区域;然后利用二次曲面的特性参数化边界点列;最后,通过径向基函数表示的插值曲面计算位于残缺区域内部的数据点,实现残缺点云数据的修复.  相似文献
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Normalized Gaussian Radial Basis Function networks   总被引:4,自引:0,他引:4  
Guido Bugmann 《Neurocomputing》1998,20(1-3):97-110
The performances of normalised RBF (NRBF) nets and standard RBF nets are compared in simple classification and mapping problems. In normalized RBF networks, the traditional roles of weights and activities in the hidden layer are switched. Hidden nodes perform a function similar to a Voronoi tessellation of the input space, and the output weights become the network's output over the partition defined by the hidden nodes. Consequently, NRBF nets lose the localized characteristics of standard RBF nets and exhibit excellent generalization properties, to the extent that hidden nodes need to be recruited only for training data at the boundaries of class domains. Reflecting this, a new learning rule is proposed that greatly reduces the number of hidden nodes needed in classification tasks. As for mapping applications, it is shown that NRBF nets may outperform standard RBFs nets and exhibit more uniform errors. In both applications, the width of basis functions is uncritical, which makes NRBF nets easy to use.  相似文献
3.
Solving large radial basis function (RBF) interpolation problem with non-customized methods is computationally expensive and the matrices that occur are typically badly conditioned. In order to avoid these difficulties, we present a fitting based on radial basis functions satisfying side conditions by least squares, although compared with interpolation the method loses some accuracy, it reduces the computational cost largely. Since the fitting accuracy and the non-singularity of coefficient matrix in normal equation are relevant to the uniformity of chosen centers of the fitted RBF, we present a choice method of uniform centers. Numerical results confirm the fitting efficiency.  相似文献
4.
Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure,thereby reducing the complexity of the algorithm.Firstly,based on the compactly supported radial basis function,the paper makes the complex quadratic function(Multiquadric,MQ for short) to be transformed and proposes a class of compactly supported MQ function.Secondly,the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction.Finally,according to this characteristic of the uneven distribution of the face markers,the markers are numbered and grouped in accordance with the density level,and then be interpolated in line with each group.The approach not only ensures the accuracy of the deformation of face local area and smoothness,but also reduces the time complexity of computing.  相似文献
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为提高船体的优化效率,以国际标准船型KCS为研究对象,以船舶总阻力和桨盘面伴流不均匀度为优化目标,建立近似模型,完成KCS船尾线型的优化,得到优化船型.通过优化结果可知:对于母型船,在满足工程约束条件下,通过船尾优化可以得到总阻力未增加、船尾流场品质有明显改善的船体线型.  相似文献
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现有的三维人脸建模方法存在三点不足:建模条件苛刻、建模精度不高和建模时间长。针对以上不足,提出明暗恢复形状(SFS)和局部形变模型融合的3D人脸建模方法。该方法利用SFS快速恢复3D人脸粗糙数据,得到3D轮廓脸;然后分别对人脸不同局部应用形变模型恢复其局部3D精确数据,并使用其对轮廓脸进行内插平滑处理重建出高精度3D人脸模型。实验结果表明:该方法能够获得较好的建模精度,在短时间内可以通过单幅真实图像重建出个性化的三维人脸模型。  相似文献
7.
This paper presents CONORBIT (CONstrained Optimization by Radial Basis function Interpolation in Trust regions), a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions, and is an extension of the ORBIT algorithm [S.M. Wild, R.G. Regis and C.A. Shoemaker, ORBIT: optimization by radial basis function interpolation in trust-regions, SIAM J. Sci. Comput. 30 (2008), pp. 3197–3219]. It uses a small margin for the RBF constraint models to facilitate the generation of feasible iterates, and extensive numerical tests confirm that such a margin is helpful in improving performance. CONORBIT is compared with other algorithms on 27 test problems, a chemical process optimization problem, and an automotive application. Numerical results show that CONORBIT performs better than COBYLA (Powell 1994), a sequential penalty derivative-free method, an augmented Lagrangian method, a direct search method, and another RBF-based algorithm on the test problems and on the automotive application.  相似文献
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