结合肤色分割与平滑的人脸图像快速美化

目的 针对目前人脸图像美化算法存在的对于细节丰富的眼睛和头发等区域处理过度平滑,美化后的图像整体美化效果较差等问题,提出一种基于肤色分割与平滑人脸图像的美化方法。方法 首先对脸部瑕疵特性,用双指数边缘保护滤波器平滑人脸图像的瑕疵,与此同时很好保持图像边缘信息;再通过利用色度直方图自适应快速检测、修正、分割肤色区域;然后利用拟合高斯羽化皮肤区域生成蒙版,融合平滑图像和原图像,保留图像头发背景等细节信息;最后基于人像美感标准,对皮肤亮度通过拟合log曲线实现快速自适应调整人脸图像亮度,增强眼睛等细节,从而快速实现人脸图像美化方法。结果 通过与其他人像美化算法相比较,在保留边缘方面,该算法更有效地对皮肤边缘上的瑕疵进行平滑,达到更好地美化人脸图像;而在时间复杂度方面,相对于前人的算法,计算速度快12倍,实现快速美化人脸图像。结论 该算法适应能力较强,对大部分人脸图像的脸部瑕疵完美去除的同时达到背景信息不变,肤色美白自然,使整体美化效果显著;尤其是细节丰富的边缘区域平滑适度,具有一定的实用性。     

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.      

中心差分卡尔曼平滑器

针对一类非线性离散系统的状态平滑问题, 本文设计了一种中心差分卡尔曼平滑器(CDKS). 文中基于最小方差估计准则, 详细推导了非线性系统的状态最优平滑递推公式, 并采用中心差分变换来近似计算状态的后验均值和协方差. 相比于传统中心差分卡尔曼滤波器(CDKF), 所设计的CDKS算法有效提高了非线性状态的估计精度, 拓展了中心差分变换的应用范围. 仿真实例验证了所提出平滑器的可行性和有效性.     

快速次优固定区间Wiener平滑器算法

为了克服带相关噪声控制系统的最优固定区间Kalman平滑算法要求较大计算负担的缺点,应用Kalman滤波方法,基于CARMA新息模型,由稳态最优Kalman平滑器导出了带相关噪声控制系统的最优固定区间Wiener递推状态平滑器,它带有系数阵指数衰减到零的高阶多项式矩阵.用截断系数矩阵近似为零的项的方法提出了相应的快速次优固定区间Wiener平滑算法.它显著地减少了计算负担,便于实时应用,还给出了截断误差公式和选择截断指标的公式.仿真例子说明了快速平滑算法的有效性.     

Distributed Kalman filter for linear system with complex multi-channel stochastic uncertain parameter and decoupled local filters

This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi-channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.     

Application of unscented R–T–S smoothing on INS/GPS integration system post processing for airborne earth observation

The Kalman Rauch–Tung–Striebel (R–T–S) smoother has been applied to fuse data from the inertial navigation system (INS) and global positioning system (GPS) for post processing, but its optimality heavily depends on linearity. For the case of in-flight startup, the INS/GPS integration is a nonlinear system with large initial attitude errors, the linear estimation approaches become inapplicable. In this paper, the Unscented R–T–S Smoother (URTSS) is utilized to deal with the nonlinear problem in the INS/GPS integration post processing, and the performance of this algorithm is compared with a similar smoother based on Extended Kalman Filter (ERTSS) through the Monto Carlo simulations. Furthermore, an INS/GPS integration system is implemented using URTSS and applied to airborne digital camera imaging. Through numerical simulation and flight test, it is shown that URTSS has obvious accuracy advantage over ERTSS in attitude estimation.     

Pole-assignment fixed-interval Kalman smoother with an exponential stability

Using the innovation analysis method in the time domain, based on the autoregressive moving average (ARMA) innovation model and white noise estimators, a pole-assignment fixed-interval steady-state Kalman smoother is presented for discrete-time linear stochastic systems. It avoids the computation of the optimal initial smoothing estimate, and can rapidly eliminate the effect of arbitrary initial smoothing estimate by assigning the poles of the smoother, with an exponentially decaying rate. Several simulation examples show its effectiveness.     

ARMA信号的自校正去卷平滑器

本文从时间序列分析观点处理自适应信号去卷问题.应用新息方法和射影理论,对于通 过线性系统被观测的未知的ARMA信号,本文提出了两类新的自校正最优去卷平滑器,包括 了一些文献中的问题和结果作为特例[1-8],可应用于数据和信号处理[1,5,7,8]、通讯[2,6]、语音识 别[3]等许多不同领域.仿真例子说明了本文结果的有效性.     

ARMA信号的自校正去卷平滑器

本文从时间序列分析观点处理自适应信号去卷问题.应用新息方法和射影理论,对于通过线性系统被观测的未知的ARMA信号,本文提出了两类新的自校正最优去卷平滑器,包括了一些文献中的问题和结果作为特例,可应用于数据和信号处理、通讯、语音识别等许多不同领域.仿真例子说明了本文结果的有效性.