基于YCbCr颜色空间背景建模与手势阴影消除

为了提高动态手势检测的精确度,本文将基于YCbCr颜色空间的混合高斯背景建模应用于动态手势识别中,并且提出手势阴影消除的有效算法。首先,对待检测视频帧通过抠图抠出手势图像,在YCb'Cr'颜色空间进行椭圆拟合,统计建立椭圆肤色模型,继而在YCbCr颜色空间进行混合高斯背景建模检测出动态手势,点乘原图像得到含有阴影的RGB手势图像,对检测出的含有阴影的手势图像利用已建立的椭圆肤色模型进行阴影消除,最后将手势图像连成视频序列。实验结果表明,该算法在复杂背景下进行动态手势的检测率可达91.4%,高出传统方法10%左右,能够满足动态手势检测基本要求,且具有较高的实用价值。     

Distributed analysis of simultaneous EEG-fMRI time-series: modeling and interpretation issues

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales.     

Significance analysis and statistical mechanics: an application to clustering

This Letter addresses the statistical significance of structures in random data: given a set of vectors and a measure of mutual similarity, how likely is it that a subset of these vectors forms a cluster with enhanced similarity among its elements? The computation of this cluster p value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple-testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.     

Speech dereverberation method based on spectral subtraction and spectral line enhancement

Speech signals recorded with a distant microphone usually are interfered by the spatial reverberation in the room, which severely degrades the clarity and intelligibility of speech. A speech dereverberation method based on spectral subtraction and spectral line enhancement is proposed in this paper. Following the generalized statistical reverberation model, the power spectrum of late reverberation is estimated and removed from the reverberation speech by the spectral subtraction method. Then, according to the human auditory model, a spectral line enhancement technique based on adaptive post-filtering is adopted to further eliminate the reverberant components between adjacent speech formants. The proposed method can effectively suppress the spatial reverberation and improve the auditory perception of speech. The subjective and objective evaluation results reveal that the perceptual quality of speech is greatly improved by the proposed method.     

Energy gap,clustering, and the Goldstone theorem in statistical mechanics

We prove a Goldstone-type theorem for a wide class of lattice and continuum quantum systems, both for the ground state and at nonzero temperature. For the ground state (T=0) spontaneous breakdown of a continuous symmetry implies no energy gap. For nonzero temperature, spontaneous symmetry breakdown implies slow clustering (noL ¹ clustering). The methods apply also to nonzero-temperature classical systems.Partial financial support by Fundação de Amparo à Pesquisa do Estado de São Paulo.Partial financial support by CNPq.     

Improving statistical keyword detection in short texts: Entropic and clustering approaches

In the last years, two successful approaches have been introduced to tackle the problem of statistical keyword detection in a text without the use of external information: (i) The entropic approach, where Shannon’s entropy of information is used to quantify the information content of the sequence of occurrences of each word in the text; and (ii) The clustering approach, which links the heterogeneity of the spatial distribution of a word in the text (clustering) with its relevance. In this paper, first we present some modifications to both techniques which improve their results. Then, we propose new metrics to evaluate the performance of keyword detectors based specifically on the needs of a typical user, and we employ them to find out which approach performs better. Although both approaches work well in long texts, we obtain in general that measures based on word-clustering perform at least as well as the entropic measure, which needs a convenient partition of the text to be applied, such as chapters of a book. In the latter approach we also show that the partition of the text chosen affects strongly its results. Finally, we focus on short texts, a case of high practical importance, such as short reports, web pages, scientific articles, etc. We show that the performance of word-clustering measures is also good in generic short texts since these measures are able to discriminate better the degree of relevance of low frequency words than the entropic approach.     

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.     

Background subtraction: I. General behaviour of Tougaard-style backgrounds in AES and XPS

An analysis is made of Tougaard-style backgrounds for homogeneous materials to show how parameters interact together and comprise the general inelastic scattering background in electron spectroscopy. It is shown that insight is usefully gained by rewriting the Tougaard universal loss function in terms of two parameters, the characteristic energy, E₁, of the exponential decay observed in multiple self-convolutes of the Tougaard universal single loss function, and the centroid energy of the single loss function. Close fits to the measured background may be made over a wide energy range for only one value of E₁ which defines a unique relation between Tougaard’s B and C values but does not give the unique values themselves. For the single value of E₁, the centroid energy may be varied such that the background subtracted spectra range from those shown by Tougaard, where the intrinsic shake-up and losses may constitute two thirds of the peak intensity, to spectra similar to those of Jo in which all of the intrinsic losses are removed. Studies of Al X-ray excited Sc, Cu, Sm and Au photoelectron spectra, each of which has an extensive range of peaks, show that the relative intensities of the peaks are unaffected by the choice of the value of even though the absolute peak areas may change by a factor of 3.     

基于灰度聚类算法的红外图像增强研究

根据红外灰度图像的特点，提出了一种基于K-均值聚类的图像增强的新算法。该算法首先根据具体图像确定K值，其次对红外图像的辐射温度数据进行统计学习，把不同温度值按升序排列，然后按等差原则选取温度值作为初始聚类中心，再依据初始聚类中心采用K-均值聚类算法对温度进行聚类，最后由聚类结果对图像进行自适应增强。通过对红外灰度图像进行实验，得到了满意的结果: 对比直方图均衡，具有更丰富的图像细节信息和层次感，视觉效果更好。     

Multiparticle entangled quantum states and modeling of thermodynamic statistical distributions

Thermodynamic equilibrium of a system is considered as a consequence of quantum entanglement of the vacuum state of the system. An explicit mathematical model of multiparticle entangled pure quantum states is developed and analyzed. Within the framework of this model, the measurement process gives rise to probability distributions that exactly correspond to thermal equilibrium of the system in a thermostat.     

Self-organizing map clustering based on continuous multiresolution entropy

The detection of changes in the parameter values of a nonlinear dynamic system is a branch of study with multiple applications. In this paper, we explore a variant of an automatic detector and clustering of slight parameter variations in nonlinear dynamic systems proposed by Torres et al. [Automatic detection of slight changes in nonlinear dynamical systems using multiresolution entropy tools, Int. J. Bifurc. Chaos 11(4) (2001) 967–981]. The new method takes the advantages of the continuous multiresolution entropy to localize slight changes in the parameters, and uses self-organizing maps to quantify and cluster these changes. We discuss the performance of this method while applied to automatic segmentation of natural and synthetic diphthongs in the presence of additive noise. Our results show the potentiality of the proposed method.     

Consistency in statistical moments as a test for bubble cloud clustering

Frequency dependent measurements of attenuation and/or sound speed through clouds of gas bubbles in liquids are often inverted to find the bubble size distribution and the void fraction of gas. The inversions are often done using an effective medium theory as a forward model under the assumption that the bubble positions are Poisson distributed (i.e., statistically independent). Under circumstances in which single scattering does not adequately describe the pressure field, the assumption of independence in position can yield large errors when clustering is present, leading to errors in the inverted bubble size distribution. It is difficult, however, to determine the existence of clustering in bubble clouds without the use of specialized acoustic or optical imaging equipment. A method is described here in which the existence of bubble clustering can be identified by examining the consistency between the first two statistical moments of multiple frequency acoustic measurements.     

Speech emotion recognition based on statistical pitch model

A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech. The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85% if the traditional parameters are utilized.     

基于聚类算法的血管直径测量研究

血管形态的变化与疾病密切相关,血管直径是血管形态的主要参数之一,测量血管直径有助于疾病的筛查与预防。提出一种基于聚类算法的血管直径测量方法,对微血管进行测量。大多数显微血管图像(如光学显微成像或光声显微成像)中存在噪声,通过非线性变换函数对显微图像进行增强;使用训练后的U-Net网络模型进行图像分割;利用结合聚类算法以及射线算法的测量方法对分割得到的血管进行测量,得到血管直径。实验表明,算法与传统测量结果一致(P0.05),与传统算法相比,本算法的测量精度得到提升,将测量误差由4.21%降低至2.27%,满足血管测量的准确度需求。     

An empirical approach to clustering based on atomic energies

We present an empirical scheme that relates the Ω parameter, a quantity important in the context of clustering, to atomic p-energies. This scheme allows the determination of hitherto experimentally unknown Ω values by extrapolating from the known ones.     

基于快速模糊C均值聚类算法的红外图像分割

 针对模糊C均值(FCM)聚类图像分割需要预先知道类别数及计算量较大的问题,提出了新的快速FCM改进方法。首先,利用边缘信息进行邻域搜索得到种子像素;通过区域生长快速获得区域分割类别数和对应的聚类中心值,并将图像分成确定类别的区域和未确定类别的区域;最后利用所得的聚类中心值和 FCM算法对未确定类别区域进行聚类。实验证明,本文提出的改进方法大大减少了计算量,显著提高了图像分割速度,而且由于聚类考虑了相邻像素点的关系,图像分割结果能够清晰地保留目标轮廓,提高了图像分割的质量。     

Optical signal quality metric based on statistical moments and Laguerre expansion

A new and straightforward optical signal quality metric, which is based on the approximation of the analytical probability density function by its Laguerre expansion, is proposed. This expansion uses statistical moments to approximate the true statistics, but, unlike the well-known Gaussian approximation, higher order moments can be used as well. The quality of the proposed metric is discussed and the metric is compared with the Gaussian approximation. It is shown that the new metric provides not only a good estimation of the bit error rate in the range of practical interest but, contrary to the Gaussian approximation, correctly estimates optimum decision threshold and accurately pictures the shape of the noise distribution.     

基于聚类和最佳指数的快速高光谱波段选择方法

最佳指数法是常用的高光谱图像数据波段选择方法,但存在运算时间过长的问题。运用K-means聚类算法,对最佳指数方法进行了改进,提出了聚类最佳指数法,并进行了一系列伪装目标识别的对比实验。实验结果表明,与最佳指数法相比,改进后的方法在保证目标分类精度的前提下,运算速度提高了数十倍;与单纯使用K-means聚类运算相比,不仅运算时间缩短,而且分类精度有所提高。利用改进算法能够在伪装环境下更加快速有效地识别目标。     

基于Voigt峰的快速光谱建模算法

间接硬建模算法是近年来提出的一种新型光谱定量分析技术,适用于分析混合物光谱与待测成分之间的非线性关系,并能够很好地解决光谱波段上的重叠峰问题。这种算法在建立定量分析模型之前先要对光谱曲线进行数学建模,即通过多个Voigt峰函数的叠加形式来描述所测得的光谱曲线。光谱建模的精确程度直接影响定量分析模型的准确性,而光谱曲线往往需要由几十个甚至上百个Voigt峰的叠加而成,因此这一过程实际是一个高维寻优问题,需要较大的运算开销并有可能令优化问题呈现"病态性"。为了降低优化问题维数,文章通过对重叠峰的判断提出了一种改进型的光谱建模算法,实验表明改进型方法与传统方法相比具有运行时间短、模型精确度高等优点。