Ear recognition is a new kind of biometric identification technology now.Feature extraction is a key step in pattern recognition technology,which determines the accuracy of classification results.The method of single ...Ear recognition is a new kind of biometric identification technology now.Feature extraction is a key step in pattern recognition technology,which determines the accuracy of classification results.The method of single feature extraction can achieve high recognition rate under certain conditions,but the use of double feature extraction can overcome the limitation of single feature extraction.In order to improve the accuracy of classification results,this paper proposes a new method,that is,the method of complementary double feature extraction based on Principal Component Analysis(PCA)and Fisherface,and we apply it to human ear image recognition.The experiment was carried out on the ear image library provided by the University of Science and Technology Beijing.The results show that the ear recognition rate of the proposed method is significantly higher than the single feature extraction using PCA,Fisherface,or Independent component analysis(ICA)alone.展开更多
As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the hol...As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the holographic diffraction label recognition.Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction,in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features.The HSV(Hue Saturation Value)tensor and the HOG(Histogram of Oriented Gradient)tensor are used to represent the color information and gradient information of holographic diffraction label,respectively.Meanwhile,the tensor decomposition is performed by high order singular value decomposition,and tensor decomposition matrices are obtained.Taking into consideration of the different recognition capabilities of decomposition matrices,we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA(Principal Component Analysis)sub-space,then,the sub-space performs KNN(K-Nearest Neighbors)classification is performed.The effectiveness of our fusion strategy is verified by experiments.Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system.展开更多
为了进一步提高电动汽车轮毂电机轴承状态识别技术的高效可靠性,提出一种基于双核支持向量数据描述(double kernel based support vector data description,简称DK-SVDD)的轮毂电机轴承状态识别方法。首先,针对轮毂电机轴承样本数据结...为了进一步提高电动汽车轮毂电机轴承状态识别技术的高效可靠性,提出一种基于双核支持向量数据描述(double kernel based support vector data description,简称DK-SVDD)的轮毂电机轴承状态识别方法。首先,针对轮毂电机轴承样本数据结构混杂致使SVDD识别率较低问题,通过一定的比例权重将径向基(radial basis function,简称RBF)核函数和高斯差分(difference of Gaussians,简称DOG)核函数结合构建DK核函数;其次,根据最优二叉树原理逐层设计状态识别分类器,并搭建DK-SVDD轮毂电机轴承状态识别模型,同时使用粒子群优化算法对模型参数寻优以提高DK-SVDD的学习能力和泛化能力;最后,基于轮毂电机轴承台架试验数据,验证所提方法的有效性和优越性。结果表明:针对轮毂电机轴承目标状态识别,DK-SVDD方法平均训练时间为0.0655 s,平均状态识别率为97.06%;与采用RBF或DOG核函数相比,DK-SVDD方法在多种工况下可以有效提高状态识别率并降低训练时间。展开更多
基金National Key R&D Program of China(No:2019YFD0901605).
文摘Ear recognition is a new kind of biometric identification technology now.Feature extraction is a key step in pattern recognition technology,which determines the accuracy of classification results.The method of single feature extraction can achieve high recognition rate under certain conditions,but the use of double feature extraction can overcome the limitation of single feature extraction.In order to improve the accuracy of classification results,this paper proposes a new method,that is,the method of complementary double feature extraction based on Principal Component Analysis(PCA)and Fisherface,and we apply it to human ear image recognition.The experiment was carried out on the ear image library provided by the University of Science and Technology Beijing.The results show that the ear recognition rate of the proposed method is significantly higher than the single feature extraction using PCA,Fisherface,or Independent component analysis(ICA)alone.
基金This work was mainly supported by Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department.(No.LGG18F020013,No.LGG19F020016,LGF21F020006).
文摘As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the holographic diffraction label recognition.Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction,in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features.The HSV(Hue Saturation Value)tensor and the HOG(Histogram of Oriented Gradient)tensor are used to represent the color information and gradient information of holographic diffraction label,respectively.Meanwhile,the tensor decomposition is performed by high order singular value decomposition,and tensor decomposition matrices are obtained.Taking into consideration of the different recognition capabilities of decomposition matrices,we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA(Principal Component Analysis)sub-space,then,the sub-space performs KNN(K-Nearest Neighbors)classification is performed.The effectiveness of our fusion strategy is verified by experiments.Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system.
文摘为了进一步提高电动汽车轮毂电机轴承状态识别技术的高效可靠性,提出一种基于双核支持向量数据描述(double kernel based support vector data description,简称DK-SVDD)的轮毂电机轴承状态识别方法。首先,针对轮毂电机轴承样本数据结构混杂致使SVDD识别率较低问题,通过一定的比例权重将径向基(radial basis function,简称RBF)核函数和高斯差分(difference of Gaussians,简称DOG)核函数结合构建DK核函数;其次,根据最优二叉树原理逐层设计状态识别分类器,并搭建DK-SVDD轮毂电机轴承状态识别模型,同时使用粒子群优化算法对模型参数寻优以提高DK-SVDD的学习能力和泛化能力;最后,基于轮毂电机轴承台架试验数据,验证所提方法的有效性和优越性。结果表明:针对轮毂电机轴承目标状态识别,DK-SVDD方法平均训练时间为0.0655 s,平均状态识别率为97.06%;与采用RBF或DOG核函数相比,DK-SVDD方法在多种工况下可以有效提高状态识别率并降低训练时间。
