Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding   总被引：1，自引：0，他引：1  

ZHANG Jian ZHUANG Yue-ting 《浙江大学学报(A卷英文版)》2007,8(4):550-558

In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.  相似文献   

基于RBF神经网络的智能入侵检测系统     

方桂娟  林锦贤 《三明学院学报》2005,22(2):201-204

对当前入侵检测技术进行了分析并讨论了现有入侵检测系统的不足，论述了神经网络应用于入侵检测中的优势。由于RBF网络具有最佳逼近性质，给出了一种基于RBF神经网络的智能人侵检测系统模型。  相似文献   

Study of CNG/diesel dual fuel engine’s emissions by means of RBF neural network     

刘震涛  费少梅 《Journal of Zhejiang University. Science. B》2004,(8)

INTRODUCTION The serious environmental pollution and theenergy crisis all over the world has caused the de-velopment of the lower pollution and lower energyconsumption automobile to become major researchgoal. An engine using compressed natural gas(CNG) as fuel has outstanding advantages of higherefficiency and lower pollution. The CNG/dieselDFE for the city bus could also obviously reducethe pollution of the city air, especially for the bigcities. So the research on the combustion…  相似文献   

一种基于RBF的时序缺失数据修复方法     

黄华东  方小勇  陈政  洪俊  黄樱 《怀化师专学报》2013,(5):40-43

提出了一种基于RBF的时序缺失数据修复方法,利用RBF构建模板数据和当前存在缺失的数据之间的训练关系,并通过该训练关系修复缺失数据．实验表明,该方法能够应用于刚性体以及非刚形体运动或形变追踪,是一种有效的时序缺失数据修复方法．  相似文献   

径向基函数神经网络在集装箱吞吐量预测中的应用     

王锡淮  鲍敏中 《上海海事大学学报》1999,(3)

针对一类具有时间序列特性的数据,构造一种基于径向基函数（ＲＢＦ） 神经网络的预测模型,并将该模型应用于上海港集装箱吞吐量的预测。  相似文献   

Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network     

YANG Xiao-hua HUANG Jing-feng WANG Jian-wen WANG Xiu-zhen LIU Zhan-yu 《浙江大学学报(A卷英文版)》2007,8(6):883-895

Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.  相似文献   

基于径向基神经网络的我国外汇储备规模预测     

滕昕  李树民 《太原理工大学高等教育研究》2006,24(4):32-34

基于MATLAB6．5平台编程，运用非线性径向基神经网络对我国外汇储备规模进行预测分析，以我国历年外汇储备数据为训练样本，进行网络训练与检验，结果表明，我国外汇储备存在超常规增长，径向基神经网络具有良好的预测性能。  相似文献   

基于RBF神经网络滤波的噪声消除     

董翠英  王志秦 《唐山学院学报》2007,20(6):39-40,52

RBF神经网络噪声抵消系统不需要关于输入信号和噪声的先验知识,非线性映射能力强。采用自适应噪声抵消基本原理,构造RBF神经网络自适应滤波器,然后针对该系统,建立Simulink仿真模型。仿真结果表明,该方法具有良好的噪声抑制能力。  相似文献   

RBF神经网络在道路交通事故预测中的应用     

孟杰  吕正兵 《常熟理工学院学报》2012,26(2):86-90

分析了RBF神经网络在事故预测领域的适应性,提出了采用RBF神经网络建立道路事故预测模型．利用我国1990—2009年道路交通事故数据,运用RBF神经网络建立了非线性回归预测模型,并利用该模型预测了2000—2010年的道路交通事故死亡人数,结果显示该模型具有较高的可靠性,可为我国道路交通管理提供数据支持。  相似文献   

基于RBF和ARIMA模型的区域高等教育规模预测研究     

郑凤霞 《乐山师范学院学报》2013,(12):6-9

结合RBF网络模型和ARIMA模型预测的优点,构建基于RBF网络模型和ARIMA模型的混合模型,对四川省高等教育规模预测问题进行研究。采用构建ARIMA模型得出的预测值对RBF网络模型的预测值进行修正,提高了区域高等教育规模预测的精度。  相似文献