蚁群和粒子群混合优化SVM的钢板表面缺陷分类研究

热轧带钢表面的温度高、生产速度快,辐射光强,并且存在着水、氧化铁皮、光照不均等现象,难以通过人工进行表面质量在线检测。针对当前国内某钢厂热轧钢板表面缺陷检测仍由人工离线完成、缺陷识别准确率低的生产问题,充分利用大量图像信息,提出一种图像处理与蚁群和粒子群混合优化支持向量机结合的缺陷分类方法。首先,融合局部二值模式和局部相位量化两种特征提取方式的优点,进行钢板缺陷图片的特征提取,采用蚁群和粒子群优化出支持向量机的惩罚参数和核函数参数进行钢板表面的缺陷分类。最后采用Matlab仿真平台,将蚁群和粒子群混合优化的支持向量机分类模型与传统的支持向量机分类模型进行仿真对比分析。试验结果表明,采用蚁群和粒子群混合优化的支持向量机分类模型的分类精度高于传统的支持向量机模型。     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

磁瓦表面缺陷机器视觉检测与识别方法

针对磁瓦生产过程中表面缺陷检测的重要性和人工检测的弊端,研究基于机器视觉的磁瓦表面缺陷自动检测与识别方法.为解决磁瓦表面缺陷种类多、对比度低、图像中存在磨痕纹理背景和整体亮度不均匀等难点,定义扫描线梯度,其标准差与扫描线灰度标准差构成特征向量,提出基于两类支持向量机的图像分割方法来判别和提取缺陷;并提出一种改进的多类支持向量机方法,对缺陷进行分类识别,解决了多类支持向量机存在不可分区域的问题,提高了分类器的准确性和有效性.实验结果表明,该方法能准确快速地提检测磁瓦表面各区域的各类缺陷,检出率可达到96％以上,识别率超过91％.     

基于支持向量机改进算法的船舶类型识别研究

利用船舶目标辐射噪声DEMON谱特征,采用改进的支持向量机算法,实现了对船舶目标的分类识别研究。针对支持向量机算法对噪声比较敏感和最优分类面求解时约束较多不利于支持向量机最优分类面寻优的问题,在保持支持向量稀疏性和应用径向基核函数的条件下,对支持向量机算法在松弛变量和决策函数两方面进行了改进,提出了基于径向基核函数的齐次决策二阶损失函数支持向量机改进算法,并应用于利用船舶目标辐射噪声DEMON谱进行船舶目标类型分类识别实验。理论分析、数据仿真与实验结果表明,该改进算法实现了在二次规划中的较少约束条件下最优分类面求解,具有模型参数寻优空间广阔、总体分类性能优的特点,其分类性能优于原支持向量机算法,是一种适合于船舶辐射噪声DENOM分类识别的有效的支持向量机改进算法。     

基于支持向量机分类策略的多晶硅电池片色差检测

针对复杂颜色和纹理特征条件下,多晶硅电池片上的色差检测问题,提出了一种基于支持向量机分类策略的多晶硅电池片色差检测方法。首先对预处理后电池片图像进行颜色模型转换和通道分离,利用Otsu方法对单通道图像进行阈值分割处理,并计算各阈值图像的区域对比度,然后根据区域对比度情况选择合适的阈值图像,利用阈值图像所提供的信息提取图像特征;最后使用支持向量机分类器来判别电池片是否存在色差缺陷。实验结果表明提出的色差检测算法可以实现多晶硅电池片色差高效检测,色差缺陷检测的准确度、误检率和检测时间分别达到96.88%, 5％和109ms。     

基于遗传算法优化支持向量机的超声图像缺陷分类

超声图像缺陷在分类时由于存在样本数量少、样本类别多、不易区分等问题,分类的准确率较低。针对这些问题,提出了基于遗传算法优化支持向量机的超声图像缺陷分类方法。该方法首先通过图像处理提取超声图像缺陷的特征数据,然后训练支持向量机作为超声图像缺陷分类器,最后采用遗传算法优化参数求得最优的分类器。实验结果表明,提出的超声图像缺陷分类器在识别率方面优于其他方法的分类器,综合识别率达到了90%,可以有效地辅助工作人员对超声图像缺陷进行分类识别。     

基于支持向量机的印品缺陷分类方法

目的研究印品图像的各类形状缺陷,建立基于支持向量机(Support vector machine,SVM)的印品形状缺陷分类模型。方法对印品进行符合人眼视觉特性的缺陷识别,并对提取缺陷进行特征分析。将特征数据导入支持向量机进行训练学习,SVM分类器对缺陷图像进行测试。结果分类器对点缺陷和面缺陷的识别率为100%,对线缺陷的分类准确率达93.94%。结论基于SVM的缺陷分类方法能较好地满足印品质量检测的需求。     

组合核函数支持向量机在水中目标识别中的应用

论文研究了支持向量机核函数构成条件以及不同核函数的特性，结合水中目标识别技术特点，提出了一种组合核函数支持向量机的方法。提取了基于小波变换的舰船辐射噪声奇异性、尺度-过零、尺度-能量特征，对水中目标进行了SVM分类识别。研究表明，基于组合核函数的支持向量机分类识别效果优于单独核函数的支持向量机识别效果。     

基于新核函数的支持向量机在物体分类中的应用

在基于视的物体识别中,将图像的局部信息引入到图像的相似性度量,提出了一种新的图像距离度量,并把它嵌入到支持向量机的核函数中,得到了一种新的核函数--基于局部卡方距离(Chi-square distance)的核函数.物体分类实验结果表明,新算法优于非线性支持向量机,区别张量一阶分解(DTROD),稀疏网络模型(SNW)等方法.     

基于新核函数的支持向量机在物体分类中的应用

在基于视的物体识别中,将图像的局部信息引入到图像的相似性度量,提出了一种新的图像距离度量,并把它嵌入到支持向量机的核函数中,得到了一种新的核函数—基于局部卡方距离（Chi—square distance）的核函数。物体分类实验结果表明,新算法优于非线性支持向量机,区别张量一阶分解（DTROD）,稀疏网络模型（SNW）等方法。     

一种基于差影法及SVM的在线纸病检测分类方法

目的 解决目前纸病分类算法存在的实时性差、难以适应生产线在线检测要求等问题。方法 提出一种基于差影法和支持向量机的在线纸病检测分类方法。首先使用差影法来判断纸张是否含有纸病;对含有纸病的纸张进行打标机打标,同时存储图像,提取纸病区域外接矩形的特征向量;最后使用支持向量机对纸病进行分类。结果 将该方法与已有的BP神经网络以及朴素贝叶斯方法进行对比可知,分类正确率高于目前已有的分类方法,对于4种纸病的分类正确率均在90%以上,而且实时性好,更加适合于在线检测。结论 该方法可以有效地对纸病进行分类,满足生产线实时检测分类的要求。     

A mass classification and image retrieval model for mammograms

In this paper, a breast tissue density classification and image retrieval model is studied and a model for the data reduction is presented. This model is based on two-directional two-dimensional principal component analysis ((2D)²PCA) technique, and a support vector machine (SVM) with the radial basis function (RBF) for mammographic images classification and retrieval. The model is formed based on breast density, according to the categories defined by the breast imaging-reporting and data system (BIRADS) which is a standard on the assessment of mammographic images and is tested on the Mammographic Image Analysis Society (MIAS) database. The five-fold cross-validation has been used for the parameters selection in SVM to avoid the over-fitting error in the data classification. The average precision rates of the model are in the range from 87·34% to 99·12%.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

基于交叉验证SVM的网络入侵检测

针对传统入侵检测系统漏报率和误报率高的问题,将支持向量机(SVM)应用于入侵检测中,提出了在SVM学习过程中引入交叉验证的方法,采用径向基函数(RBF)作为核,将训练集分成若干子集,每一子集使用其它子集训练得到的分类器进行测试,获得RBF的两个最佳参数后,将其应用于最终的分类器.实验结果表明,该方法能够有效检测入侵攻击,具有更高的检测率和更强的泛化能力,同时具有较低的误报率和漏报率,可以有效地运用于入侵检测系统中.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Providing an efficient intelligent transportation system through detection,tracking and recognition of the region of interest in traffic signs by using non-linear SVM classifier in line with histogram oriented gradient and Kalman filter approach

With a focus on new researches in the area of intelligent transportation systems (ITS), an efficient approach has been investigated here. Based on the present view point, analysis of traffic signs are first considered via intelligence based approach, which is carried out through three main stages including detection, tracking and recognition, respectively, in this research. The key role of detection is to identify traffic signs by classification of road sign shapes in accordance with their signatures. This classification consists of four different shapes of circle, semicircle, triangle and square, as well. The linear classification of traffic sign is also carried out via support vector machine (SVM) by using one against all (OAA), since the present SVMs classifiers realized via linear kernel. The next step is to track traffic sign. It should be noted that this technique is now developed to reduce the searching mode in case of the whole area to be optimized its computational processing, consequently. This research work is investigated by realizing Kalman filter approach, where, finally, in recognition step, a feature of the region of interest (ROI) has been extracted for SVM classification. Histogram of oriented gradient (HOG) is realized in organizing the approach, as long as Gaussian kernel is also developed for non-linear SVM classifier.     

Earlier detection of cancer regions from MR image features and SVM classifiers

In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016