Because the equivalence principle forbids local mass density, we cannot formulate general relativistic mass as an integral over mass density as in Newtonian gravity. This century-old problem was addressed forty years ...Because the equivalence principle forbids local mass density, we cannot formulate general relativistic mass as an integral over mass density as in Newtonian gravity. This century-old problem was addressed forty years ago by Penrose, and many papers have since extended the concept. Currently there is no satisfactory physical understanding of the nature of quasi-local mass. In this paper I review the key issues, the current status, and propose an alternative interpretation of the problem of local mass and energy density for gravity systems from an information perspective.展开更多
Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously...Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.展开更多
Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In thi...Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.展开更多
目的针对传统局部方向模式(LDP)在特征提取的充分性、对光照和噪声等的鲁棒性以及识别时间长短这3方面不能同时取得一个很好的平衡效果,提出了一种双空间局部方向模式(DSLDP)的人脸识别方法。方法首先,将图像3×3邻域像素灰度值与8...目的针对传统局部方向模式(LDP)在特征提取的充分性、对光照和噪声等的鲁棒性以及识别时间长短这3方面不能同时取得一个很好的平衡效果,提出了一种双空间局部方向模式(DSLDP)的人脸识别方法。方法首先,将图像3×3邻域像素灰度值与8个Kirsch模板算子卷积,得到8个方向的边缘响应值,然后,将近邻边缘响应值之间相应作差,对应8个方向的边缘响应差值,将两组值取绝对值,取各自最大值的方向编码成一个二位八进制数,产生DSLDP码。最后,在人脸描述阶段将人脸图像进行分块并把每块转换成DSLDP图,再对DSLDP图进行直方图统计,并利用信息熵对每块进行加权,将所有子块的直方图连接生成人脸特征,再通过PCA进行降维,用最近邻分类器分类识别。结果在剑桥大学Olivetti实验室(ORL)、Aleix Martinez and Robert Benavente(AR)和中国科学院(CAS-PEAL)的人脸图像数据库进行实验,相比局部方向模式(LDP)、显著型局部方向模式(SLDP)、增强型局部方向模式(ELDP)、局部方向数字模式(LDN)、差值型局部方向模式(DLDP)、中心对称局部方向模式(CSLDP)和梯度中心对称局部方向模式(GCSLDP),DSLDP具有更好的识别性能。5幅测试样本时,在ORL库上取得了97.82%的平均识别率,在AR光照、表情、遮挡A和遮挡B库分别取得了98.00%、98.33%、99.33%、87.67%的平均识别率,在CAS-PEAL光照、表情和饰物库分别取得了99.33%、95.33%、90.00%的平均识别率。结论 1)该方法既考虑了近邻边缘响应值的外在变化,也考虑了近邻边缘响应值之间的内在变化,通过将强度空间和梯度空间人脸特征信息结合使人脸特征得到更加充分的提取。2)DSLDP只考虑邻边缘响应值和边缘响应差值的最大值情况,突出了主要边缘梯度信息,同时又避免了不重要信息的干扰,相比同类基于局部方向模式的单一人脸识别算法,对光照、表情、噪声、遮挡等情况表现出展开更多
局部切空间排列算法(local tangent space alignment,LTSA)是一种经典的非线性流形学习方法,能够有效地对非线性分布数据进行降维,但它无法学习局部高曲率数据集.针对此问题,给出了描述数据集局部曲率的参数,并提出一种局部最小偏差空...局部切空间排列算法(local tangent space alignment,LTSA)是一种经典的非线性流形学习方法,能够有效地对非线性分布数据进行降维,但它无法学习局部高曲率数据集.针对此问题,给出了描述数据集局部曲率的参数,并提出一种局部最小偏差空间排列(locally minimal deviation spacealignment,LMDSA)算法.该算法考虑到局部切空间低鲁棒性的缺陷,在计算局部最小偏差空间的同时,能够发现数据的局部高曲率现象,通过参数控制及邻域间的连接信息,减少计算局部高曲率空间的可能,进而利用空间排列技术进行降维,手工流形及真实数据集的实验证实了该算法学习局部高曲率数据集的有效性.展开更多
文摘Because the equivalence principle forbids local mass density, we cannot formulate general relativistic mass as an integral over mass density as in Newtonian gravity. This century-old problem was addressed forty years ago by Penrose, and many papers have since extended the concept. Currently there is no satisfactory physical understanding of the nature of quasi-local mass. In this paper I review the key issues, the current status, and propose an alternative interpretation of the problem of local mass and energy density for gravity systems from an information perspective.
文摘Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.
基金the National Natural Science Foundation of China(No.61004088)the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)
文摘Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.
文摘目的针对传统局部方向模式(LDP)在特征提取的充分性、对光照和噪声等的鲁棒性以及识别时间长短这3方面不能同时取得一个很好的平衡效果,提出了一种双空间局部方向模式(DSLDP)的人脸识别方法。方法首先,将图像3×3邻域像素灰度值与8个Kirsch模板算子卷积,得到8个方向的边缘响应值,然后,将近邻边缘响应值之间相应作差,对应8个方向的边缘响应差值,将两组值取绝对值,取各自最大值的方向编码成一个二位八进制数,产生DSLDP码。最后,在人脸描述阶段将人脸图像进行分块并把每块转换成DSLDP图,再对DSLDP图进行直方图统计,并利用信息熵对每块进行加权,将所有子块的直方图连接生成人脸特征,再通过PCA进行降维,用最近邻分类器分类识别。结果在剑桥大学Olivetti实验室(ORL)、Aleix Martinez and Robert Benavente(AR)和中国科学院(CAS-PEAL)的人脸图像数据库进行实验,相比局部方向模式(LDP)、显著型局部方向模式(SLDP)、增强型局部方向模式(ELDP)、局部方向数字模式(LDN)、差值型局部方向模式(DLDP)、中心对称局部方向模式(CSLDP)和梯度中心对称局部方向模式(GCSLDP),DSLDP具有更好的识别性能。5幅测试样本时,在ORL库上取得了97.82%的平均识别率,在AR光照、表情、遮挡A和遮挡B库分别取得了98.00%、98.33%、99.33%、87.67%的平均识别率,在CAS-PEAL光照、表情和饰物库分别取得了99.33%、95.33%、90.00%的平均识别率。结论 1)该方法既考虑了近邻边缘响应值的外在变化,也考虑了近邻边缘响应值之间的内在变化,通过将强度空间和梯度空间人脸特征信息结合使人脸特征得到更加充分的提取。2)DSLDP只考虑邻边缘响应值和边缘响应差值的最大值情况,突出了主要边缘梯度信息,同时又避免了不重要信息的干扰,相比同类基于局部方向模式的单一人脸识别算法,对光照、表情、噪声、遮挡等情况表现出
