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Fast algorithm for surface reconstruction from cloud data based RBF neural network
基于RBF神经网络的点云数据曲面重建快速算法

HUANG Miao,ZHANG Hai-chao,PU Jie-xin,LI Chao,
黄淼
,张海朝,普杰信,李超

计算机应用 , 2008,
Abstract: On the basis of the analysis of the existing reconstruction methods limitations, a fast neural network based algorithm for 3D surface reconstruction from cloud data was presented. Firstly, a unitary processing for the cloud data was made. And then the contour lines were extracted and the surface based contour lines were segmented. The method can directly from the neural network value matrix get the right curve control points or surface control mesh, and through neural network values restriction achieve the right curve or patch of smooth connection. Experimental results show that this method can quickly obtain good shape Mesh.
Census and Segmentation-Based Disparity Estimation Algorithm Using Region Merging  [PDF]
Viral H. Borisagar, Mukesh A. Zaveri
Journal of Signal and Information Processing (JSIP) , 2015, DOI: 10.4236/jsip.2015.63018
Abstract: Disparity estimation is an ill-posed problem in computer vision. It is explored comprehensively due to its usefulness in many areas like 3D scene reconstruction, robot navigation, parts inspection, virtual reality and image-based rendering. In this paper, we propose a hybrid disparity generation algorithm which uses census based and segmentation based approaches. Census transform does not give good results in textureless areas, but is suitable for highly textured regions. While segment based stereo matching techniques gives good result in textureless regions. Coarse disparities obtained from census transform are combined with the region information extracted by mean shift segmentation method, so that a region matching can be applied by using affine transformation. Affine transformation is used to remove noise from each segment. Mean shift segmentation technique creates more than one segment of same object resulting into non-smoothness disparity. Region merging is applied to obtain refined smooth disparity map. Finally, multilateral filtering is applied on the disparity map estimated to preserve the information and to smooth the disparity map. The proposed algorithm generates good results compared to the classic census transform. Our proposed algorithm solves standard problems like occlusions, repetitive patterns, textureless regions, perspective distortion, specular reflection and noise. Experiments are performed on middlebury stereo test bed and the results demonstrate that the proposed algorithm achieves high accuracy, efficiency and robustness.
Prediction of Tourist Quantity Based on RBF Neural Network  [cached]
HuaiQiang Zhang,JingBing Li
Journal of Computers , 2012, DOI: 10.4304/jcp.7.4.965-970
Abstract: Tourist quantity is an important factor deciding economic benefits and sustainable development of tourism. Thus tourist quantity prediction becomes the important content of tourism development planning. Based on the tourist quantity of Hainan province for more than twenty years, this paper establishes tourist quantity prediction model according to RBF neural network [1], in which the principle and algorithm of RBF neural network is used. And this paper also predicts the future tourist quantity of Hainan province. The Matlab emulational result of RBF neural network model shows based on RBF neural network tourist quantity prediction model can exactly predict the future tourist quantity of Hainan province, thus providing a new idea and mean for tourist quantity prediction.
Surface Reconstruction from Scattered Point via RBF Interpolation on GPU  [PDF]
Salvatore Cuomo,Ardelio Gallettiy,Giulio Giuntay,Alfredo Staracey
Computer Science , 2013,
Abstract: In this paper we describe a parallel implicit method based on radial basis functions (RBF) for surface reconstruction. The applicability of RBF methods is hindered by its computational demand, that requires the solution of linear systems of size equal to the number of data points. Our reconstruction implementation relies on parallel scientific libraries and is supported for massively multi-core architectures, namely Graphic Processor Units (GPUs). The performance of the proposed method in terms of accuracy of the reconstruction and computing time shows that the RBF interpolant can be very effective for such problem.
Synchronization Scheme for Uncertain Chaotic Systems via RBF Neural Network
CHEN Mou,JIANG Chang-Sheng,WU Qing-Xian,CHEN Wen-Hua,
陈谋
,姜长生,吴庆宪,陈文华

中国物理快报 , 2007,
Abstract: A sliding mode adaptive synchronization controller is presented with a neural network of radial basis function (RBF) for two chaotic systems. The uncertainty of the synchronization error system is approximated by the RBF neural network. The synchronization controller is given based on the output of the RBF neural network. The proposed controller can make the synchronization error convergent to zero in 5s and can overcome disruption of the uncertainty of the system and the exterior disturbance. Finally, an example is given to illustrate the effectiveness of the proposed synchronization control method.
Research on Spatial Estimation of Soil Property Based on Improved RBF Neural Network  [PDF]
Jianbo Xu,Quanyuan Tan,Lisheng Song,Kai Hao
International Journal of Computer Science Issues , 2013,
Abstract: To seek optimal network parameters of Radial Basis Function (RBF) Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial interpolation is conducted for arable soil property under different sampling scales in the study area. The estimation result is superior to RBF Neural Network method without optimization and geostatistical method in terms of the fitting capacity and interpolation accuracy. Compared with the result of space estimation by RBF Neural Network method without optimization, among the 5 schemes, the forecast errors of RBF Neural Network optimized by genetic algorithm reduce greatly. Mean absolute error (MAE) reduces 0.4868 on the average and root-mean-square error (RMSE) reduces 1.492 on the average. Therefore, RBF Neural Network method optimized by genetic algorithm can gain the information about regional soil property spatial variation more accurately and provides technical support for arable land quality evaluation, accurate farmland management and rational application of fertilizer.
Vehicle License Plate Recognition Based on Text-line Construction and Multilevel RBF Neural Network  [cached]
Baoming Shan
Journal of Computers , 2011, DOI: 10.4304/jcp.6.2.246-253
Abstract: License plate localization and character segmentation and recognition are the research hotspots of vehicle license plate recognition (VLPR) technology. A new method to VLPR is presented in this paper. In license plate localization section, Otsu binarization is operated to get the plate-candidates regions, and a text-line is constructed from the candidate regions. According to the text-line construction result and the characteristics of the license plate character arrangement, the license plate location will be determined. And then the locally optimal adaptive binarization is utilized to make more accurate license plate localization. After the license plate localization, the segment method of vertical projection information with prior knowledge is used to slit characters and the statistical features are extracted. Then the multilevel classification RBF neural network is used to recognize characters using the feature vector as input. The results show that this method can recognize characters precisely and improve the ability of license plate character recognition effectively.
Surface Water Quality Evaluation Using BP and RBF Neural Network  [cached]
Qinghua Luan,Changjun Zhu
Journal of Software , 2011, DOI: 10.4304/jsw.6.12.2528-2534
Abstract: It is very important to evaluate water quality in environment protection. Water environment is a complicated system, traditional methods can not meet the demands of water environment protection. In view of the deficiency of the traditional methods, a BP neural network model and a RBF neural network model are proposed to evaluate water quality. The proposed model was applied to evaluate the water quality of 10 sections in Suzhou river. The evaluation result was compared with that of the RBF neural network method and the reported results in Suzhou river. It indicated that the performance of proposed neural network model is practically feasible in tha application of water quality assessment and its operation is simple.
On Structure Design for RBF Neural Network Based on Information Strength
基于信息强度的RBF神经网络结构设计研究

HAN Hong-Gui,QIAO Jun-Fei,BO Ying-Chun,
韩红桂
,乔俊飞,薄迎春

自动化学报 , 2012,
Abstract: Based on the systemic investigation on the feedforword neural network, for the problem of the structure design of the RBF neural network, a new flexible structure design method is used for RBF neural network in this paper. By computing the output-information (OI) of the hidden neurons and the multi-information (MI) of the hidden nodes and output nodes, the hidden nodes in the RBF neural network can be inserted or pruned, thus the topology of the network can be modulated. This method can effectively solve the structure design of the RBF neural network. The grad-descent method for the parameter adjusting ensures the exactitude of the flexible RBF neural network (F-RBF). The structure of the RBF neural network is self-organizing, and the parameters are self-adaptive. In the end, the proposed F-RBF is used for approximating the classical non-linear functions and modelling key parameters of the wastewater treatment process. The results show that the F-RBF obtains a favorable dynamic character response and the approximating ability. Especially, comparied with the minimal resource allocation networks (MRAN), the generalized growing and pruning RBF (GGAP-RBF) and the self-organizing RBF (SORBF), the proposed algorithm is more effective in terms of training time, generalization, and neural network structure.
The OLS Algorithm Rased on Energy Distribution for RBF Neural Network
基于能量分布的RBF神经网络OLS算法

XIAO Guo-Qiang ZHANG Wei-Qun,
肖国强
,张为群

计算机科学 , 2004,
Abstract: : Due to its structural simplicity, the radial basis function (RBF)neural network has been widely used for approximation and classification. The role of hidden layer neurons of a RBF neural network can be interpreted as a function which maps input patterns from a nonlinear separable space to a linear separable space. In the present study, we use OLS algorithm based on energy distribution to train RBF. The experiment results indicate that the performance of the proposed method is better than that of standard OLS.
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