Evaluation of echo features of ultrasonic flaws and its intelligent pattern recognition

In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples. A method which is based on the xtatislical hypothesis testing and used for feature evaluation and optimum subset selection was explored. Thus, the dimensionality reduction of feature space was brought out, and simultaneously the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feature vectors. The average recognition rate of three type flaws was about 87.6%, and the best recognition rate amounted to 97%.

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis （PCA）. After obtaining the region of interest （ROI） for each finger image in the pre- processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve （K-L） transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

Real-Time Face Detection and Recognition in Complex Background

This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.

A Feasible Approach for Automatic Detection and Recognition of the Bengalese Finch Songnotes and Their Sequences

The Bengalese finch song has been widely studied for its unique features and similarity to human language. For com-putational analysis the songs must be represented in songnote sequences. An automated approach for this purpose is highly desired since manual processing makes human annotation cumbersome, and human annotation is very heu-ristic and easily lacks objectivity. In this paper, we propose a new approach for automatic detection and recognition of the songnote sequences via image processing. The proposed method is based on human recognition process to visually identify the patterns in a sonogram image. The songnotes of the Bengalese finch are dependent on the birds and similar pattern does not exist in two different birds. Considering this constraint, our experiments on real birdsong data of different Bengalese finch show high accuracy rates for automatic detection and recognition of the songnotes. These results indicate that the proposed approach is feasible and generalized for any Bengalese finch songs.

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.

Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.

Radon CLF:A Novel Approach for Skew Detection Using Radon Transform

In the digital world,a wide range of handwritten and printed documents should be converted to digital format using a variety of tools,including mobile phones and scanners.Unfortunately,this is not an optimal procedure,and the entire document image might be degraded.Imperfect conversion effects due to noise,motion blur,and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition(OCR)systems.In Document Image Analysis Systems(DIAS),skew estimation of images is a crucial step.In this paper,a novel,fast,and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function(CLF),so-called Radon CLF,was proposed.The Radon CLF model aims to take advantage of the properties of Radon spaces.The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space.Several significant performance indicators,including Mean Square Error(MSE),Mean Absolute Error(MAE),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Measure(SSIM),Accuracy,and run-time,were taken into consideration when assessing the performance of our model.In addition,a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model.Both two-dimensional image signal and the Radon space have been used in our simulations to compare the noise effect.Obtained results show that the proposed method is more effective than other approaches already in use,with an accuracy of roughly 99.87%and a run-time of 0.048(s).The introduced model is far more accurate and timeefficient than current approaches in detecting image skew.

铆接铝合金板铆钉失效缺陷检测方法研究

针对车身用铝合金板内部铆钉缺陷特征提取难度大、缺陷类型与程度识别准确率低的问题,提出一种基于高斯卷积深度信念网络与双向长短期记忆网络相结合的铆钉失效缺陷诊断模型与检测方法。首先,面向5种铆钉断裂缺陷设计试件并搭建自动检测系统,通过规划和调整探头姿态有效地降低提离效应对检测信号的影响。其次,设计双网络融合诊断模型提取和学习多维度缺陷特征信息,解决检测曲线中由时序变化特性和空间分布状态表征的缺陷信息提取难题。实验结果表明,与传统卷积网络及单一深度信念网络相比,优化后算法诊断模型的平均准确率为99.85%,相比提升了14.54%,且具有良好的通用性和鲁棒性,可实现铆钉内部缺陷的在线诊断。

Study on the Essence of Optimal Statistically Uncorrelated Discriminant Vectors and Its Application to Face Recognition

A study has been made on the essence of optimal uncorrelated discriminant vectors. A whitening transform has been constructed by means of the eigen decomposition of the population scatter matrix, which makes the population scatter matrix be an identity matrix in the transformed sample space no matter whether the population scatter matrix is singular or not. Thus, the optimal discriminant vectors solved by the conventional linear discriminant analysis (LDA) methods are statistically uncorrelated. The research indicates that the essence of the statistically uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristics of the proposed method is that the obtained optimal discriminant vectors are not only orthogonal but also statistically uncorrelated. The proposed method is applicable to all the problems of algebraic feature extraction. The numerical experiments on several facial databases show the effectiveness of the proposed method.

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

基于人工神经网络的电力变压器声纹识别技术

针对变压器运行状态声纹识别的应用需求以及BP神经网络识别模型准确率较低等问题,提出了一种改进人工神经网络的变压器声纹识别技术。该技术以变压器声纹信号中的MFCC系数作为模型的输入特征向量,在BOA算法中引入动态权重因子和变异因子对BP神经网络权值和阈值进行寻优,开展声纹识别。实验结果表明,利用变压器声纹信号的32维MFCC特征系数可使识别准确率达到90%以上,优化后算法的运算速度比PSO-BP神经网络与BOA-BP神经网络提高了9.24%和8.64%,具有更高的运算效率和识别准确率。

基于异常检测的内部威胁识别系统开发

随着信息技术的快速发展,内部威胁已经成为许多组织面临的严重挑战。为了有效应对这一挑战,文中提出了一种基于异常检测的内部威胁识别系统。该系统利用先进的异常检测算法,结合了日志分析和行为模式识别的技术,能及时、准确地识别潜在的内部威胁行为。在实验中,使用了实际的数据集进行验证,并与传统的威胁检测方法进行了比较。实验结果表明,该系统在识别内部威胁方面具有明显优势,表现出了较高的准确性和效率。

一种具有统计不相关性的最佳鉴别矢量集

在模式识别领域，基于Ｆｉｓｈｅｒ鉴别准则函数的Ｆｏｌｅｙ－Ｓａｍ ｍ ｏｎ 最佳鉴别矢量集技术有着重大的影响．特征抽取的一般原则是最好抽取模式的不相关的特征，而Ｆｏｌｅｙ－Ｓａｍ ｍ ｏｎ 最佳鉴别矢量集的诸鉴别特征是统计相关的．文中提出了一种具有统计不相关性的最佳鉴别矢量集，并给出了计算公式．对ＯＲＬ人脸图像数据库作了实验，实验结果表明，具有统计不相关性的最佳鉴别矢量集有较强的特征抽取能力，优于Ｆｏｌｅｙ－Ｓａｍ ｍ ｏｎ

局部二值模式方法研究与展望

针对当前局部二值模式(Local binary pattern,LBP)方法表现出的理论和实际应用价值,系统综述了在纹理分析和分类、人脸分析和识别以及其他检测与应用中的各种LBP方法.首先,简要概述了LBP方法的原理,主要分析了LBP方法中的阈值操作并介绍了统一模式和旋转不变性模式.其次,分别对纹理分析和分类中的LBP方法、人脸分析和识别中的LBP方法以及其他检测与应用中的LBP方法等三个方面进行了详细的梳理和评述.最后,分析了LBP方法在应用中依旧存在的重要问题并指出了未来的研究方向.

超声检测缺陷分类的小波分析与神经网络方法

根据金属超声检测中缺陷脉冲回波为非稳态信号的特点，提出了一种基于小波变换和模式识别技术的缺陷定性分类方法.重点研究了利用小波变换提取反映缺陷性质的特征值以及运用模式识别技术对特征值进行缺陷定性识别的方法.为验证上述方法，设计了实验系统，同时对信号的采集、异常信号的剔除等问题进行了研究.利用实际焊接试样进行了实验，经小波变换提取缺陷特征值，然后采用BP(back propagation)神经网络，使缺陷的定性分类获得了较高的准确率.研究结果表明该方法可在一定程度上降低人为因素对缺陷定性识别的影响，获得较好的缺陷分类效果.

基于纹理分析的脂肪肝B超图像识别

目的为B超诊断脂肪肝建立计算机辅助诊断手段。方法通过分析正常肝和脂肪肝B超图像的纹理特征 ,包括灰度共生矩阵的角二阶矩、熵和反差分矩统计特征 ,组成特征矢量 ,再用k 平均聚类算法和自组织特征映射人工神经网络算法对特征矢量进行分类处理。结果k 平均聚类算法对正常肝的识别率为 63.6% ,对脂肪肝的识别正确率达 90 .9% ;自组织特征映射人工神经网络对正常肝的识别正确率达 84.8% ,对脂肪肝的识别正确率达 90 .9%。结论本文中建立的方法能较肉眼更精确地反映正常肝和脂肪肝B超图像的特征 ,如果再结合医生的临床经验能大大提高脂肪肝的诊断准确性。

路面破损自动识别的一种新算法

针对路面破损分类这一难题,提出了一种基于破损密度因子的路面破损分类新算法。对破损密度因子进行了定义和仿真实验,仿真结果表明其对5种常见的路面破损状况的分类效果非常理想。为了进行对比,文中还选择了美国博士论文中的PROXIMITY算法进行比较,两种方法对相同的10几万幅路面样本进行分类试验,试验结果表明,笔者提出的基于破损密度因子的路面破分类方法,整体优于PROXIMITY方法的分类效果。

网络脚本病毒的统计分析方法

基于统计学习理论,提出了脚本病毒的统计分析方法.其主要思想是,对脚本病毒样本代码进行明文的统计分析,得到其关键字的分布概率,以及附加统计信息后,利用该知识识别未知网络病毒.实验结果表明本方法对于未知网络脚本病毒具有很高的识别率.