Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space

This paper proposes a novel texture segmentation approach using independent-scale component-wise Riemannian-covariance Gaussian mixture model (ICRGMM) in Kullback-Leibler (KL) measure based multi-scale nonlinear structure tensor (MSNST) space. We use the independent-scale distribution and full-covariance structure to replace the covariant-scale distribution and 1D-variance structure used in our previous research. To construct the optimal full-covariance structure, we define the full-covariance on KL, Euclidean, log-Euclidean, and Riemannian gradient mappings, and compare their performances. The comparison experiments demonstrate that the Riemannian gradient mapping leads to its optimum properties over other choices when constructing the full-covariance. To estimate and update the statistical parameters more accurately, the component-wise expectation-maximization for mixtures (CEM²) algorithm is proposed instead of the originally used K-means algorithm. The superiority of the proposed ICRGMM has been demonstrated based on texture clustering and Graph Cuts based texture segmentation using a large number of synthesis texture images and real natural scene textured images, and further analyzed in terms of error ratio and modified F-measure, respectively.     

Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions

We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two heavy-tailed distributions namely Student’s t and exponential power distributions with different tailednesses and kurtoses are used and they are represented in scale mixture of normal and scale mixture of uniform respectively. The proposed model is capable of describing the trend and meanwhile the mixing parameters in the scale mixture representations can detect the outlying observations. Simulations and real data analysis are performed to investigate the properties of the models.     

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

This paper studies a heavy-tailed stochastic volatility (SV) model with leverage effect, where a bivariate Student-t distribution is used to model the error innovations of the return and volatility equations. Choy et al. (2008) studied this model by expressing the bivariate Student-t distribution as a scale mixture of bivariate normal distributions. We propose an alternative formulation by first deriving a conditional Student-t distribution for the return and a marginal Student-t distribution for the log-volatility and then express these two Student-t distributions as a scale mixture of normal (SMN) distributions. Our approach separates the sources of outliers and allows for distinguishing between outliers generated by the return process or by the volatility process, and hence is an improvement over the approach of Choy et al. (2008). In addition, it allows an efficient model implementation using the WinBUGS software. A simulation study is conducted to assess the performance of the proposed approach and its comparison with the approach by Choy et al. (2008). In the empirical study, daily exchange rate returns of the Australian dollar to various currencies and daily stock market index returns of various international stock markets are analysed. Model comparison relies on the Deviance Information Criterion and convergence diagnostic is monitored by Geweke’s convergence test.     

基于FPGA的混合高斯背景建模实现

为了解决PC机上高清视频运动目标检测的实时性瓶颈问题,设计了一种基于FPGA的运动目标检测系统.系统采用基于自适应混合高斯背景模型的背景差分法,对环境扰动具有很好的适应性.本设计应用于1 280×1 024高清视频的运动目标检测,针对硬件实现的特点,对OpenCV混合高斯背景模型算法进行改进和适当的参数定点化,设计了适...     

一种基于场景区分算法的背景抽取方法

视频监控面对的场景具有静态的成分和缓变的成分.为了从视频流中稳定地抽取较精确的背景,提出了一种基于场景区分的背景抽取算法.首先通过混合高斯模型对视频序列中稳定的成分进行建模,然后采用Kalman滤波模型对视频序列中变化的成分进行建模,最后采用场景的区分算法将2种模型结果进行处理从而得到背景.实验结果表明:该方法能从视频...     

基于混合高斯模型的新型目标检测系统

针对传统混合高斯模型计算量过大及其在非平稳背景下存在的问题,提出一种新型运动目标检测系统。该系统引入模型等权重初始化策略,改善了视频检测初始阶段的效果;通过基于线性均差的模型匹配方法,减少了对方差的运算次数,有效减少了模型的计算量;加入干扰信息处理模块,以增强模型在复杂背景下的生存能力。经实验验证,新型系统准确检测出了视频初始阶段的多运动目标,可使进入场景后停留的目标快速融入背景,并能有效克服非平稳背景的扰动。实验结果表明该系统相比经典模型,准确性和鲁棒性均有明显改善。     

基于双约简的GMM说话人辨识

如何能够快速准确的实现说话人识别是说话人识别研究的一个重要环节。提出双约简GMM的说话人确认方法,对语音参数进行核K-均值聚类,提取聚类中心的语音特征矢量作为训练数据,并对其进行fisher比约简,提取具有区分力强的维数。实验表明该文提取方法可以减少训练时间,并提高系统的鲁棒性。     

基于ARM+uC/OS-Ⅱ的超声波水煤浆粒度检测仪的设计

根据超声波在水煤浆中传播的声学特性的变化与水煤浆粒度之间的函数关系,基于低功耗32位嵌入式微处理器LPC2210与实时操作系统uC/OS-Ⅱ,文章提出了一种智能型水煤浆粒度快速检测仪的设计方案,并在测时电路里运用了锁相环技术,提高了超声传播时间的测量精确度。实际运行证明,该检测仪能满足实际测定水煤浆粒度的要求。     

Mixture model for face-color modeling and segmentation

In this paper, we propose a general methodology for face-color modeling and segmentation. One of the major difficulties in face detection and retrieval is partial face extraction due to highlights, shadows and lighting variations. We show that a mixture-of-Gaussians modeling of the color space, provides a robust representation that can accommodate large color variations, as well as highlights and shadows. Our method enables to segment within-face regions, and associate semantic meaning to them, and provides statistical analysis and evaluation of the dominant variability within a given archive.