提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA...提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA与Fisher线性判别FLD(Fisher Linear Discriminate)结合起来,利用了以前仅用于降维的PCA特征和FLD特征相融合进行掌纹识别。基于PolyU掌纹库的实验结果表明,该方法不仅有更高的识别率,而且维数更低。展开更多
The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient ...The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.展开更多
多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图...多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy ofSciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.展开更多
文摘提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA与Fisher线性判别FLD(Fisher Linear Discriminate)结合起来,利用了以前仅用于降维的PCA特征和FLD特征相融合进行掌纹识别。基于PolyU掌纹库的实验结果表明,该方法不仅有更高的识别率,而且维数更低。
文摘The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.
文摘多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy ofSciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.
