面向三坐标测量机应用的检测特征自动提取和识别

为了解决三坐标测量机(CMM)中手工输入检测信息的问题,提出了自动提取和识别检测特征的方案。应用特征技术,实现了基于CAD模型公差特征的自动提取。利用CAD模型中几何要素标识唯一的特点,建立了STEP和QDAS中性文件的匹配,解决了几何信息和检测信息在CAD和CMM之间的传递和识别问题。在Unigraphics上进行了二次开发,使其能自动输出匹配好的STEP和QDAS文件,并能被CMM软件识别和应用。

空间目标红外微弱信号特征提取检测技术

由于受空间传输距离．大气噪声、接收系统噪声等因素的影响，使得CCD接收到的目标跟踪图像信号背景噪声很大，目标几乎被噪声完全淹没，这使得在使用传统的线性门限目标检出技术时受到限制。本文提出用特征值分解技术对受噪声污染的图像进行降噪，然后对目标进行检测与跟踪，实验证明该技术能很好地抑制背景噪声，在较低的信噪比情况下也能提取出跟踪的目标。

利用边缘聚焦算法进行图像边缘线特征的提取

本文介绍了一种基于边缘聚焦的图像线状边缘特征提取与检测的方法 ,既能保证提取边缘特征的定位精度 ,又能抑制无关的细节和噪声。

基于漏洞的蠕虫特征自动提取技术研究

提出一种新的基于漏洞的蠕虫特征,其区别于传统的基于语法或语义分析的技术,对蠕虫攻击的漏洞特征进行分析,将该算法应用于检测系统中。通过实验证明,该检测系统能有效地检测出各种多态变形蠕虫。

数控加工原位检测系统中检测规划关键技术研究

原位检测对于大型薄壁结构件加工质量控制具有重要意义。为提高检测效率和实现检测自动化,对数控加工原位检测系统中检测特征提取、测点规划等关键技术进行了研究,提出了检测特征的图表示方法和一种基于规则的测点采样方法,并基于CATIA/CAA二次开发工具设计开发了一套检测规划系统,对上述功能进行了实现。实例表明,研发的系统能够快速准确地完成复杂工件的检测规划。

基于序列图像的冲压件检测系统研究

传统零件及产品的检测也越来越要求其高效和准确。简单手工操作和借助工具直接测量既不能满足生产的要求,同时重复而繁杂的工作极易导致测量的误差。本文主要探讨基于序列图像的小型冲压件的机器视觉检测的实现算法及该系统的实现,视觉检测系统的大致流程为机械平台搭建,特征提取,相机标定,误差分析。该系统在以Visual C++为软件平台。

DAMAGE CLASSIFICATION BY PROBABILISTIC NEURAL NETWORKS BASED ON LATENT COMPONENTS FOR TIME-VARYING SYSTEM

A new approach to damage classification for health monitoring of a time-varylng system is presented. The functional-series time-dependent auto regressive moving average （FS-TARMA） time series model is applied to the vibration signal observed in the time-varying system for estimating the TAR/TMA parameters and the innovation variance. These parameters are the functions of the time, represented by a group of projection coefficients on the certain functional subspace with specific basis functions. The estimated TAR/TMA parameters and the innovation variance are further used to calculate the latent components （LCs） as the more informative data for health monitoring evaluation, based on an eigenvalue decomposition technique. LCs are then combined and reduced to numerical values （NVs） as feature sets, which are input to a probabilistic neural network （PNN） for the damage classification. For the evaluation of the proposed method, numerical simulations of the damage classification for a tlme-varylng system are used, in which different classes of damage are modeled by the mass or stiffness reductions. It is demonstrated that the method can identify the damages in the course of operation and the change of parameters on the time-varying background of the system.

NEW CORNER DETECTION ALGORITHM BASED ON MULTI-FEATURE SYNTHESIS

Corner detection is a chief step in computer vision. A new corner detection algorithm in planar curves is proposed. Firstly, from the human perception, two key characteristics are given as an amendment of the traditional corner properties. Based on the two properties, the concept of the fuzzy set is introduced into a detection. Secondly, the extracted-formulae of three groups including the features of the corner subject degree are derived. Through synthesizing the features of three groups, the judgments of the corner detection, location, and optimization are obtained. Finally, by using the algorithm the detection results of several examples and feature curves for some interested parts, as well as the detection results for the test images history in references are given. Results show that the algorithm is easily realized after adopting the fuzzy set, and the detection effect is very ideal.

PROJECTION BASED STATISTICAL FEATURE EXTRACTION WITH MULTISPECTRAL IMAGES AND ITS APPLICATIONS ON THE YELLOW RIVER MAINSTREAM LINE DETECTION

Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.

工业图像在线识别方法研究

综述了图像处理技术中,图像预处理、图像特征提取与图像识别的各种现存方法及其优缺点,并针对工业图像处理要求实时性较强的特点,提出一种计算量小,判决速度快的新颖算法.

Method for Detecting Weld Feature Size Based on Line Structured Light

With the rapid development of the machining and manufacturing industry,welding has been widely used in forming connections of structural parts.At present,manual methods are often used for welding and quality inspection,with low efficiency and unstable product quality.Due to the requirements of visual inspection of weld feature size,a visual inspection system for weld feature size based on line structured light(LSL)is designed and built in this paper.An adaptive light stripe sub-pixel center extraction algorithm and a feature point extraction algorithm for welding light stripe are proposed.The experiment results show that the detection error of the weld width is 0.216 mm,the detection error of the remaining height is 0.035 mm,the single measurement costs 109 ms,and the inspection stability and repeatability of the system is 1%.Our approach can meet the online detection requirements of practical applications.

Intelligent detection method for workpiece defect based on industrial CT image

In order to solve the problem of internal defect detection in industry, an intelligent detection method for workpiece defect based on industrial computed tomography (CT) images is proposed. The industrial CT slice image is preprocessed first with the combination of adaptive median filtering and adaptive weighted average filtering by analyzing the characteristics of the industrial CT slice images. Then an image segmentation algorithm based on gray change rate is used to segment low contrast information in industrial CT images, and the feature of workpiece defect is extracted by using Hu invariant moment. On this basis, the radial basis function (RBF) neural network model is established and the firefly algorithm is used for optimization, and the intelligent identification of the internal defects of the workpiece is completed. Simulation results show that this method can effectively improve the accuracy of defect identification and provide a theoretical basis for the detection of internal defects in industry.

Vehicle Detection in Still Images by Using Boosted Local Feature Detector

Vehicle detectition in still images is a comparatively difficult task. This paper presents a method for this task by using boosted local pattern detector constructed from two local features including Haar-like and oriented gradient features. The whole process is composed of three stages. In the first stage, local appearance features of vehicles and non-vehicle objects are extracted. Haar-tike and oriented gradient features are extracted separately in this stage as local features. In the second stage, Adabeost algorithm is used to select the most discriminative features as weak detectors from the two local feature sets, and a strong local pattern detector is built by the weighted combination of these selected weak detectors. Finally, vehicle detection can be performed in still images by using the boosted strong local feature detector. Experiment results show that the local pattern detector constructed in this way combines the advantages of Haar-like and oriented gradient features, and can achieve better detection results than the detector by using single Haar-like features.

Automatic detection and removal of static shadows

In case of complex textures,existing static shadow detection and removal algorithms are prone to false detection of the pixels.To solve this problem,a static shadow detection and removal algorithm based on support vector machine(SVM)and region sub-block matching is proposed.Firstly,the original image is segmented into several superpixels,and these superpixels are clustered using mean-shift clustering algorithm in the superpixel sets.Secondly,these features such as color,texture,brightness,intensity and similarity of each area are extracted.These features are used as input of SVM to obtain shadow binary images through training in non-operational state.Thirdly,soft matting is used to smooth the boundary of shadow binary graph.Finally,after finding the best matching sub-block for shadow sub-block in the illumination region based on regional covariance feature and spatial distance,the shadow weighted average factor is introduced to partially correct the sub-block,and the light recovery operator is used to partially light the sub-block.The experimental results show the number of false detection of the pixels is reduced.In addition,it can remove shadows effectively for the image with rich textures and uneven shadows and make a natural transition at the boundary between shadow and light.

Research on Visual Detection Algorithm for Groove Feature Sizes by Means of Structured Light Projection

The visual inspection is an economical and effective method for welding. For measuring the feature sizes of grooves,a method based on line structured light is presented. Firstly,an adaptive algorithm to extract the subpixel centerline of structured light stripes is introduced to deal with the uneven width and grayscale distributions of laser stripes,which is based on the quadratic weighted grayscale centroid. By means of region-of-interest(ROI)division and image difference,an image preprocessing algorithm is developed for filtering noise and improving image quality. Furthermore,to acquire geometrical dimensions of various grooves and groove types precisely,the subpixel feature point extraction algorithm of grooves is designed. Finally, experimental results of feature size measuring show that the absolute error of measurement is 0.031—0.176 mm,and the relative error of measurement is 0.2%—3.6%.

An Effective Diagnosis of Diabetic Retinopathy with Aid of Soft Computing Approaches

DR （diabetic retinopathy） is a most probable reason of blindness in adults, but the only remedy or escape from blindness is that we have to detect DR as early. Several automated screening techniques are used to detect individual lesions in the retina. Still it takes more dependency of time and experts. To overcome those problems and also automatically detect DR in easier and faster way, we took into soft computing approaches in our proposed work. Our proposed work will discuss several amounts of soft computing algorithms, it can detect DR features （landmark and retinal lesions） in an easy manner. Processes includes are： （1） Pre-processing; （2） Optic disc localization and segmentation; （3） Localization of fovea; （4） Blood vessel segmentation; （5） Feature extraction; （6） Feature selection; Finally （7） detection of diabetic retinopathy stages （mild, moderate, severe and PDR）. Our experimental results based on Matlab simulation and it takes databases of STARE and DRIVE. Proposed effective soft computing approaches should improve the sensitivity, specificity and accuracy.

Feature extraction for latent fault detection and failure modes classification of board-level package under vibration loadings.

A feature extraction for latent fault detection and failure modes classification method of board-level package subjected to vibration loadings is presented for prognostics and health management(PHM) of electronics using adaptive spectrum kurtosis and kernel probability distance clustering. First, strain response data of electronic components is filtered by empirical mode decomposition(EMD) method based on maximum spectrum kurtosis(SK), and fault symptom vector is developed by computing and reconstructing the envelope spectrum. Second, nonlinear fault symptom data is mapped and clustered in sparse Hilbert space using Gaussian radial basis kernel probabilistic distance clustering method. Finally, the current state of board level package is estimated by computing the membership probability of its envelope spectrum. The experimental results demonstrated that the method can detect and classify the latent failure mode of board level package effectively before it happened.

Automatic malware classification and new malware detection using machine learning

The explosive growth ofmalware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware pro- grams. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules： data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import fimctions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor （SNN） clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the un- known malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.

Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries

Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machiner- ies is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independ- ent of the dynamic response signals. In this paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet （MOAMW） is proposed for enhancing the extracting performance of fault symptom. It is able to derive an opti- mal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. In this technique, two-scale similarity transform （TST） and symmetric lifting （SymLift） scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective. TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of mul- tiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing res- olution during the decomposition process. The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element beating with an outer race scrape and a gearbox with rub fault.

信号处理、分析与设计

0107865利用边缘聚焦算法进行图像边缘线特征的提取[刊]/游代安//信息工程大学学报.—2000,1(4).—99～101(K)本文介绍了一种基于边缘聚焦的图像线状边缘特征提取与检测的方法,既能保证提取边缘特征的定位精度,又能抑制无关的细节和噪声。参3Y2001-62613-116 0107866语音识别采用离散小波变换的特征提取=Feature ex-traction using discrete wavelet transform for speech recog-nition[会,英]/Tufekci,Z.& Gowdy,J.N.