Hard-limited Karhunen-Loeve transform for text independent speakerrecognition

A text independent speaker recognition system based on hard-limited eigenfunctions derived from the Karhunen-Loeve transform is proposed. Two databases, each with 100 Mandarin speakers, are collected for system evaluation. It is demonstrated that >94% correct classification rates can be achieved by the use of the first 32 hard-limited features     

Bayesian Classification of Cork Stoppers Using Class-Conditional Independent Component Analysis

In this paper, a real-time application for visual inspection and classification of cork stoppers is presented. The process of cork inspection and quality grading is based on analyzing a large set of characteristics corresponding to visual features that are related to cork porosity. We have applied a set of nonparametric and parametric classification methods for comparing and evaluating their performance in this real problem. The best results have been achieved using Bayesian classification through probabilistic modeling in a high-dimensional space. In this context, it is well known that high dimensionality represents a serious problem for density estimation. We propose a class-conditional independent component analysis representation of the data that allows an accurate estimation of the data probability density function by factorizing it. The method has achieved a success of 98% of correct classification     

基于压缩感知和深度学习的分类识别技术

雷达对空飞机目标分类可实现雷达装备获得敌机属性和类别信息,对于现代战争其重要性显得尤为突出。针对复杂电磁环境下的飞机目标分类问题,结合防空雷达的特点建立3类（固定翼、螺旋桨和直升机）飞机旋转部件调制回波模型,并理论分析了不同类型飞机目标的微动特征差异。仿真分析在复杂电磁环境下干扰对微动频谱的影响。引入压缩感知方法进行干扰条件下的微动特征稀疏恢复,采用堆栈自编码学习(SAE)方法构建深层神经网络对目标进行自动特征提取和分类识别;实录数据验证表明,本文特征提取和识别方法在干扰比例41%时识别正确率能达到75%。     

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure.     

Fingerprint classification based on learned features

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date.     

用于步态识别的多层窗口图像矩

该文提出了一种以局部性矩统计量作为步态特征描述的步态识别方法。首先提取行人二值轮廓序列,构造一种基于直方图的轮廓点分布特征检测出步态周期;然后生成彩色步态运动历史图像CGHI描述步态的空间特征和时间信息;继而设计了多层同心矩形窗口分割CGHI,提取出一组矩形环窗口的矩特征量作为步态特征,在此基础上实现了步态识别。在Soton数据库上进行了实验,提出算法的正确识别率可达87.2%,优于现有方法。     

Automatic detection and classification of grains of pollen based on shape and texture

Palynological data are used in a wide range of applications. Some studies describe the benefits of the development of a computer system to pollinic analysis. The system should involve the detection of the pollen grains on a slice, and their classification. This paper presents a system that realizes both tasks. The latter is based on the combination of shape and texture analysis. In relation to shape parameters, different ways to understand the contours are presented. The resulting system is evaluated for the discrimination of species of the Urticaceae family which are quite similar. The performance achieved is 89% of correct pollen grain classification.     

ECG analysis using nonlinear PCA neural networks for ischemiadetection

The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats     

一种具有旋转不变性的压缩域纹理图像分类方法

该文提出了一种具有旋转不变性的、基于DCT压缩域的纹理图像分类方法。此方法主要是在DCT压缩域中合理组织频率系数,使其具有与小波分解相类似的图像多分辨率结构,由此设计了抗旋转能力强的纹理图像分类算法。这些操作直接在游程长编码(RLE)形成的码流上进行,处理的数据量较少,处理速度快。实验结果表明,这种分类方法具有较高的准确性。     

Gradient and texture analysis for the classification of mammographic masses

Computer-aided classification of benign and malignant masses on mammograms is attempted in this study by computing gradient-based and texture-based features. Features computed based on gray-level co-occurrence matrices (GCMs) are used to evaluate the effectiveness of textural information possessed by mass regions in comparison with the textural information present in mass margins. A method involving polygonal modeling of boundaries is proposed for the extraction of a ribbon of pixels across mass margins. Two gradient-based features are developed to estimate the sharpness of mass boundaries in the ribbons of pixels extracted from their margins. A total of 54 images (28 benign and 26 malignant) containing 39 images from the Mammographic Image Analysis Society (MIAS) database and 15 images from a local database are analyzed. The best benign versus malignant classification of 82.1%, with an area (Az) of 0.85 under the receiver operating characteristics (ROC) curve, was obtained with the images from the MIAS database by using GCM-based texture features computed from mass margins. The classification method used is based on posterior probabilities computed from Mahalanobis distances. The corresponding accuracy using jack-knife classification was observed to be 74.4%, with Az = 0.67. Gradient-based features achieved Az = 0.6 on the MIAS database and Az = 0.76 on the combined database. The corresponding values obtained using jack-knife classification were observed to be 0.52 and 0.73 for the MIAS and combined databases, respectively.     

Studies of high-efficient and low-cost dye-sensitized solar cells

Dye-sensitized solar cell (DSSC) is a new type of photoelectric device. To commercialize DSSC successfully, it is necessary to further improve the efficiency of energy conversion and reduce its cost. Nitrogen-doped (N-doped) TiO2 photoanode, the carbon counter electrode (CE), and a new type of hybrid photoanode were investigated in this study. The conversion efficiency of the DSSC reached by 10.10% as the DSSC was fabricated with the N-doped photoanode, and this efficiency is much higher than that of the undoped-DSSC with 8.90%; as the low-cost carbon was used as CE, the efficiency of the DSSC was 7.50%, it was as samilar as that of Pt CE (7.47%); the hybrid DSSC with multilayer photoanode by the film-transfer technique achieved a panchromatic response and a superposed short circuit current density (Jsc) by using two complementary dyes.     

电信级以太网测试技术

为了比较不同电信级以太网（CE）技术方案的优劣,文章依据城域以太网论坛（MEF）定义的电信级以太网的特点,针对可扩展性、可靠性、服务质量（QoS）、业务承载能力以及业务管理等方面描述了相应的测试方案。通过实际的测试可以看到,目前的电信级以太网技术在以上几个方面均能基本满足电信级组网的需求。但是,由于不同的电信级以太网解决方案在业务承载能力、可靠性、扩展能力、服务质量（QoS）以及运营管理维护（OAM）能力方面存在着较大的差异性,在短期内难以实现完全的互联互通。     

Detection of breast masses in mammograms by density slicing and texture flow-field analysis.

We propose a method for the detection of masses in mammographic images that employs Gaussian smoothing and sub-sampling operations as preprocessing steps. The mass portions are segmented by establishing intensity links from the central portions of masses into the surrounding areas. We introduce methods for analyzing oriented flow-like textural information in mammograms. Features based on flow orientation in adaptive ribbons of pixels across the margins of masses are proposed to classify the regions detected as true mass regions or false-positives (FPs). The methods yielded a mass versus normal tissue classification accuracy represented as an area (Az) of 0.87 under the receiver operating characteristics (ROCs) curve with a dataset of 56 images including 30 benign disease, 13 malignant disease, and 13 normal cases selected from the mini Mammographic Image Analysis Society database. A sensitivity of 81% was achieved at 2.2 FPs/image. Malignant tumor versus normal tissue classification resulted in a higher Az value of 0.9 under the ROC curve using only the 13 malignant and 13 normal cases with a sensitivity of 85% at 2.45 FPs/image. The mass detection algorithm could detect all the 13 malignant tumors successfully, but achieved a success rate of only 63% (19/30) in detecting the benign masses. The mass regions that were successfully segmented were further classified as benign or malignant disease by computing five texture features based on gray-level co-occurrence matrices (GCMs) and using the features in a logistic regression method. The features were computed using adaptive ribbons of pixels across the boundaries of the masses. Benign versus malignant classification using the GCM-based texture features resulted in Az = 0.79 with 19 benign and 13 malignant cases.     

Rotation and gray-scale transform-invariant texture classificationusing spiral resampling, subband decomposition, and hidden Markov model

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (BMM). During classification, the unknown texture is matched against all the models and the best match is taken as the classification result. Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%     

通信信号分类算法研究

利用观测样本的高阶累积量特征,在累积量域内构造信号分类特征,采用支持矢量机作为分类器,在高维空间实现对GSM、CDMA和OFDM信号的分类。该算法通过提取观测样本的累积量的识别特征矢量以区分不同的信号类型,并将特征向量映射到高维空间中加以分类,首先在理论上分析了算法的正确性,并通过仿真实验进行了验证,结果表明,算法具有较好的推广能力,在较大的信噪比范围内对三种信号均有较高的识别率。     

基于互补特征和类描述的商品图像自动分类

实现电子商务中的在线商品自动分类是电子商务智能化的迫切要求。该文研究如何运用图像特征和分类算法对商品图像的一些具体信息进行自动分类,如长袖衬衫和短袖衬衫、圆领T恤与V领T恤等。图像特征采用了具有互补特性的塔式梯度方向直方图(PHOG)和塔式关键字直方图(PHOW)相结合;在分类器设计方面提出了基于图像类描述的改进最近邻分类算法。实验结果证明本算法能使2类和3类商品图像分类正确率达到70%-99%,且能够实现快速实时分类,相对于现有方法有了明显提升。     

Signal classification through multifractal analysis and complex domain neural networks

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.     

基于差异性和重要性的问句特征组合

在问答系统问句分类研究中,对问句特征进行组合有助于构造高效的问句分类器.针对当前问句分类中的特征组合问题,提出一种基于差异性和重要性的特征组合 (Diversity and Importance based Feature Combination,DIFC)方法.通过计算待组合特征与当前特征组合的错分差异度和正分差异度,以及待组合特征本身的重要度,从候选特征集中动态获取优化的特征组合.在哈工大中文问句集上对词袋绑定特征进行组合的实验结果表明,与其他特征组合方法相比,DIFC方法灵活高效,准确率更高.     

Performance analysis of wave atom transform in texture classification

Texture classification is an important application in image processing and pattern recognition such as detection of defects on the materials and diseases from the medical images. This paper presents the performance of wave atom transform on texture classification. Wave atom transform is a new multi-resolution technique that not only captures the coherence of the pattern along the oscillations, but also the pattern across the oscillations. The classification is done using a wave atom–transformed features reduced by singular value decomposition and a support vector machine. Experimental results are presented to demonstrate the effectiveness of this approach on Brodatz database, Alzheimer’s Disease Neuro Imaging database for Alzheimer’s disease classification and liver computed tomography images for tumor classification. The experimental results demonstrate that the proposed approach gives a percent correct classification of 97.29 % on Brodatz database, classification accuracy of 94 % on Alzheimer’s Disease Neuro Imaging database for Alzheimer’s disease diagnosis and 93.3 % on liver computed tomography images for tumor classification.     

BP神经网络对固井信号分类能力的研究

本文研究了BP神经网络对固井信号的分类能力．首先应用理论框模型分析了不同层状介质结构的回波，并系统深入地研究了层状介质物理参数的变化对BP神经网络分类的影响。理论研究结果表明，当各层介质参数在一定范围内变化时，不影响网络的正确分类，即BP神经网络对固井信号分类具有很强的Robust性。本文也给出了对实际困井信号的分类结果，其正确识别率在叨％以上。该研究结果反映出BP神经网络具有很强的信号分类能力，对将其用于固并信号分类的工程实际有重要价值。