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10篇 您的检索式:作者名="Songde Liu"
    题名 作者 年代 出处 被引量
1A NON-PARAMETER BAYESIAN CLASSIFIER FOR FACE RECOGNITION显示文摘A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.Liu Qingshan Lu Hanqing Ma Songde (Nat. Lab of Pattern Recognition, Inst. of Automation, Chinese Academy of Sciences, Beijing 100080) 2003Journal of Electronics(China)2003,20,5:9
2, Improving Kernel Fisher Discriminant Analysis for Face Recognition 显示文摘Qingshan Liu Hanqing Lu and Songde Ma 2004IEEE Trans on Circuits and Sistems for Video Technlogy2004,14,1:1
3Ultracompact meta-imagers for arbitrary all-optical convolution显示文摘Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence,but they are time-cost and energy consumption due to the low response of electrons.Although massless photons enable high-speed and low-loss analog convolutions,two existing all-optical approaches including Fourier fItering and Green's function have either limited functionality or bulky volume,thus restricting their applications in smart systems.Here,we report aloptical convolutional computing with a metasurface-singlet or-doublet imager,considered as the third approach,where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels.Beyond one-and two-dimensional spatial differentiation,we demonstrate real-time,parallel,and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement,which significantly enrich the toolkit of all-optical computing.Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions,meanwhile possessing good architecture compatibility with digital convolutional neural networks.Weiwei Fu Dong Zhao Ziqin Li Songde Liu Chao Tian Kun Huang 2022Light(Science & Applications)2022,11,4:1
4Improving kernel fisher discriminant analysis for face recognition 显示文摘Liu Qingshan Lu Hanqing Ma Songde 2004IEEE Transactions on Cir- cuits and Systems for Video Technology2004,14,1:1
5Improving kernel fisher discriminant analysis for face recognition 显示文摘LIU Qingshaa LU Hanqing MA Songde 2004IEEE Transactions on Circuits and Systems for Video Technology2004,14,1:1
6Improving Kernel Fisher Discriminant Analysis for Face Recognition显示文摘Liu Qingshan Lu Hanqing Ma Songde 2004IEEE Transactions on Circuits and Systems for Video Technology2004,14,1:1
7Head tracking using shapes and adaptive color histograms 显示文摘Liu Qingshan Ma Songde Lu Hangqing 2002Computer Science and Technology (S1000-9000)2002,17,6:1
8Three new Cu(Ⅱ)-Ln(Ⅲ) heterometallic coordination polymers constructed from quinolinic acid and nicotinic acid: Synthesis, structures, and magnetic properties显示文摘Three new Cu(II)-Ln(Ⅲ) heterometallic coordination polymers based on two N-heterocyclic carboxylic ligands, {[LnCu(L1)2(L2)(H2O)2]·mH2O}n (Ln = La(1), Nd(2), Gd(3), m = 2 (for 1), 1 (for 2, 3), H2L1 = quinolinic acid, HL2 = nicotinic acid), have been synthesized and characterized. 1 has a two-dimensional (2D) layer structure with a Schl?fli symbol of (44.62), while complexes 2 and 3 are isostructural and have three-dimensional (3D) structures with a Schl?fli symbol of (3.4.5)2(32.42.52.614.74.83.9)(32.63.7) of 3-nodal net. Magnetic investigations suggest that antiferromagnetic coupling exists between NdⅢ and CuII in 2, while weak ferromagnetic coupling between GdⅢ and CuII in 3. The difference of magnetic properties between 2 and 3 has been discussed.LIU SuiJun SONG WeiChao XUE Li HAN SongDe ZENG YongFei WANG LiFu BU XianHe 2012中国科学:化学2012,42,6:0
9Three new Cu(Ⅱ)-Ln(Ⅲ) heterometallic coordination polymers constructed from quinolinic acid and nicotinic acid: Synthesis, structures, and magnetic properties显示文摘Three new Cu(Ⅱ)-Ln(Ⅲ) heterometallic coordination polymers based on two N-heterocyclic carboxylic ligands, {[LnCu(L1)2(L2)(H2O)2]·mH2O}n (Ln = La(1), Nd(2), Gd(3), m = 2 (for 1), 1 (for 2, 3), H2L1 = quinolinic acid, HL2 = nicotinic acid), have been synthesized and characterized. 1 has a two-dimensional (2D) layer structure with a Schlfli symbol of (44.62), while complexes 2 and 3 are isostructural and have three-dimensional (3D) structures with a Schlfli symbol of (3.4.5)2(32.42.52.614.74.83.9)(32.63.7) of 3-nodal net. Magnetic investigations suggest that antiferromagnetic coupling exists between NdIII and CuII in 2, while weak ferromagnetic coupling between GdIII and CuII in 3. The difference of magnetic properties between 2 and 3 has been discussed.LIU SuiJun SONG WeiChao XUE Li HAN SongDe ZENG YongFei WANG LiFu BU XianHe 2012Science China Chemistry2012,55,6:0
10Syntheses, structures and magnetic properties of Fe_6 and Fe_(12) ferric wheels显示文摘Starting from the same Fe3O(RCO2)6 complex and using a similar synthesis strategy, the Fe6 and Fe12 wheels were obtained by utilizing two designed ligands, 2-amino-2-methyl-1,3-propanediol(L1) and 1,3-propanediol(L2), respectively. A biological buffer reagent, Bis-Tris(2-[bis-(2-hydroxyethyl)amino]-2-(hydroxymethyl)propane-1,3-diol) was first introduced in the synthesis of the Fe12 wheel, playing a vital role in adjusting the acidity of reaction environment. Magnetic studies on both Fe6 and Fe12 wheels revealed strong antiferromagnetic coupling between the spins on FeIII ions.Guojian Ren Yanqing Liu Zhenxin Zhao Suijun Liu Songde Han Jian Xu Xianhe Bu 2015Science China(Information Sciences)2015,58,12:0
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