We theoretically demonstrate the imaging properties of a complex two-dimensional(2D) face-centered square lattice photonic crystal(PC) made from germanium cylinders in air background. The finitedifference time-domain(...We theoretically demonstrate the imaging properties of a complex two-dimensional(2D) face-centered square lattice photonic crystal(PC) made from germanium cylinders in air background. The finitedifference time-domain(FDTD) method is employed to calculate the band structure and simulate image construction. The band diagram of the complex structure is significantly compressed. Negative refraction occurs in the second energy band with negative phase velocity at a frequency of 0.228(2πc/a), which is lower than results from previous studies. Lower negative refraction frequency leads to higher image resolution. Numerical results show that the spatial resolution of the system reaches 0.7296λ, which is lower than the incident wavelength.展开更多
A novel face recognition method based on fusion of spatial and frequency features was presented to improve recognition accuracy. Dual-Tree Complex Wavelet Transform derives desirable facial features to cope with the v...A novel face recognition method based on fusion of spatial and frequency features was presented to improve recognition accuracy. Dual-Tree Complex Wavelet Transform derives desirable facial features to cope with the variation due to the illumination and facial expression changes. By adopting spectral regression and complex fusion technologies respectively, two improved neighborhood preserving discriminant analysis feature extraction methods were proposed to capture the face manifold structures and locality discriminatory information. Extensive experiments have been made to compare the recognition performance of the proposed method with some popular dimensionality reduction methods on ORL and Yale face databases. The results verify the effectiveness of the proposed method.展开更多
为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。...为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。展开更多
The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classifi...The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.展开更多
文摘We theoretically demonstrate the imaging properties of a complex two-dimensional(2D) face-centered square lattice photonic crystal(PC) made from germanium cylinders in air background. The finitedifference time-domain(FDTD) method is employed to calculate the band structure and simulate image construction. The band diagram of the complex structure is significantly compressed. Negative refraction occurs in the second energy band with negative phase velocity at a frequency of 0.228(2πc/a), which is lower than results from previous studies. Lower negative refraction frequency leads to higher image resolution. Numerical results show that the spatial resolution of the system reaches 0.7296λ, which is lower than the incident wavelength.
基金National Natural Science Foundation of China(No.61004088)Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission,China(No.09JC1408000)
文摘A novel face recognition method based on fusion of spatial and frequency features was presented to improve recognition accuracy. Dual-Tree Complex Wavelet Transform derives desirable facial features to cope with the variation due to the illumination and facial expression changes. By adopting spectral regression and complex fusion technologies respectively, two improved neighborhood preserving discriminant analysis feature extraction methods were proposed to capture the face manifold structures and locality discriminatory information. Extensive experiments have been made to compare the recognition performance of the proposed method with some popular dimensionality reduction methods on ORL and Yale face databases. The results verify the effectiveness of the proposed method.
基金教育部新世纪优秀人才支持计划(the Program for New Century Excellent Talents in University No.NCET-06-0298)辽宁省高等学校优秀人才支持计划(the Program for Liaoning Excellent Talents in University No.RC-05-07,No.2006R06)+2 种基金辽宁省教育厅科学研究计划(the Program for Study of Science of the Educational Department of Liaoning Province No.05L020)大连市科学技术计划(the Programfor Dalian Science and Technology No.2005A10GX106)大连大学辽宁省智能信息处理重点实验室开放课题(the Open Fund of LiaoningKey Lab of Intelligent Information Processing,Dalian University No.2005-9)
文摘为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。
基金supported by the Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.JB190501)Science and Technology Innovation Team of Shaanxi Province(No.2019TD-002)National Natural Science Foundation of China(No.11774277)。
文摘The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.
