基于机器视觉的随机纹理瓷砖的分选系统

针对日益加快的瓷砖生产速度与缓慢的人工分选速度之间不协调导致的瓷砖出产效率低下的问题,提出了以机器视觉软件HALCON 11.0为软件开发平台的结合瓷砖颜色、纹理特征提取的算法,以及针对多分类问题的改进多层感知器神经网络算法(MLPNN).首先对拍摄到的瓷砖图像进行去噪预处理,在HSI颜色空间中提取瓷砖的色调(Hue)特征并计算反映瓷砖的纹理特征的灰度共生矩阵(GLCM)和灰度幅值分布特征,再将得到的特征作为多层感知器的神经网络输入层神经元,然后设计以softmax为激活函数的多层感知器神经网络来进行模式匹配,并与BP神经网络模式匹配方法进行对比,最终搭建出具有简单人机交互界面的随机纹理瓷砖的分选实验样机.实验结果表明:本系统对实验的各类随机纹理瓷砖的分选准确率都在90%以上,具有较高的分选准确率,能应用于瓷砖生产实践.     

基于纹理和BP神经网络的SAR图像分类

研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明：纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。     

An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills,Sudan

The effectiveness of spectral and textural information in the identification of surface rock types in an arid region, the Red Sea Hills of Sudan, is evaluated using spectral information from the six Landsat TM optical bands and textural features derived from Shuttle Imaging Radar-C (SIR-C) C-band HH polarization data. An initial classification is derived from Landsat TM data alone using three classification algorithms, Gaussian maximum likelihood, a multi-layer feed-forward neural network and a Kohonen self-organizing feature map (SOM), to generate lithological maps, with classification accuracy being measured using a confusion matrix approach. The feed-forward neural net produced the highest overall classification accuracy of 57 per cent and was, therefore, selected for the second experiment, in which texture measures from SIR-C C-band HH-polarized synthetic aperture radar (SAR) data are added to selected TM spectral features. Four methods of measuring texture are employed, based on the Fourier power spectrum, grey level co-occurrence matrix (GLCM), multi-fractal measures, and the multiplicative autoregressive random field (MAR) model. The use of textural information together with a subset of the TM spectral features leads to an increase in classification accuracy to almost 70 per cent. Both the MAR model and the GLCM matrix approach perform better than Fourier and multi-fractal based methods of texture characterization.     

基于灰度共生矩阵的纹理特征提取①

纹理广泛存在于自然界中,是物体表面最本质的属性。纹理分析技术一直是热门的研究领域,纹理特征提取作为纹理分析的首要任务更是人们研究的焦点。针对五种木材纹理提出了用灰度共生矩阵的方法提取纹理特征,通过MATLAB仿真实现,结果表明由灰度共生矩阵产生的四个纹理特征能有效的描述木材的纹理特征,具有较好的鉴别能力。     

基于遗传算法的海底沉积物纹理特征优化方法

为了提高水下机器人对海底沉积物的自主分类感知能力,解决特征冗余问题,对利用遗传算法优化海底沉积物纹理特征进行了研究。以基于灰度共生矩阵和分形理论提取多种海底沉积物视觉纹理特征实现海底沉积物分类识别为背景,提出利用遗传算法对纹理特征项进行优化选择以实现对提取特征的降维,并将降维后的特征项作为自组织映射神经网络模型的输入,对海底沉积物进行视觉分类,提高水下机器人作业时的环境感知能力。实验结果表明,相对于未优化的纹理特征,优化后的纹理特征在海底沉积物分类识别中具有更优的分类效果。     

基于TFBP网络的人脸皮肤纹理识别方法

为了可观地评价皮肤的质地,采用图像纹理分析中的空间灰度共生矩阵法提取皮肤纹理图像的重要特征,建立一种新的人脸皮肤纹理的测量识别模型.首先对采集到图像进行预处理,然后采用空间灰度共生矩阵法提取纹理图像的5个特征,最后通过TFBP网络对皮肤纹理图像的训练与分类识别实验结果很好的证明了这种纹理分析与测量方法的有效性.     

基于中值-游程共生矩阵的纹理特征提取

提出了一种基于中值-游程共生矩阵模型的纹理特征提取方法.该方法利用了图像的灰度信息和等灰度游程长度信息,通过计算图像的中值矩阵和游程矩阵,从而计算出中值-游程共生矩阵,来提取图像的特征.仿真结果表明,该方法能有效地分割出纹理图像上区域特性不同的纹理,且分割效果优于等灰度游程矩阵和灰度共生矩阵.     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

基于EBP-OP算法与纹理特征的图像检索

基于纹理特征的图像检索在多媒体数据库管理和多媒体通信传输中得到越来越多的重视。文章提出了一种基于图像纹理特征与多层前馈神经网络EBP-OP算法的图像检索方法。首先运用灰度共生矩阵提取图像的纹理特征,然后用EBP-OP算法对提取的纹理特征进行分类,并在此基础上实现一组纹理图像的检索,实验证明这种方法是有效的。     

Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution

An abdominal aortic aneurysm (AAA) is a localized abnormal enlargement of the abdominal aorta with fatal consequences if not treated on time. The endovascular aneurysm repair (EVAR) is a minimal invasive therapy that reduces recovery times and improves survival rates in AAA cases. Nevertheless, post-operation difficulties can appear influencing the evolution of treatment. The objective of this work is to develop a pilot computer-supported diagnosis system for an automated characterization of EVAR progression from CTA images. The system is based on the extraction of texture features from post-EVAR thrombus aneurysm samples and on posterior classification. Three conventional texture-analysis methods, namely the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), the gray level difference method (GLDM), and a new method proposed by the authors, the run length matrix of local co-occurrence matrices (RLMLCM), were applied to each sample. Several classification schemes were experimentally evaluated. The ensembles of a k-nearest neighbor (k-NN), a multilayer perceptron neural network (MLP-NN), and a support vector machine (SVM) classifier fed with a reduced version of texture features resulted in a better performance (A_z = 94.35 ± 0.30), as compared to the classification performance of the other alternatives.     

激光淬火加工质量视觉检测系统研究

针对金属表面激光离散淬火质量检测的需求,基于OpenCV设计了一种淬火斑尺寸测量和表面纹理检测的视觉检测系统。深入研究了相关图像处理算法,对Canny边缘检测算法进行了改进,实现了图像轮廓的选择性提取,得到了激光淬火斑连通域的几何尺寸,采用灰度共生矩阵计算淬火斑纹理信息。并根据三种不同能量输入背景下进行激光离散淬火产生的淬火斑,提出了一种双阈值判定方法来实现其质量检测,平均检测用时为10.1ms,检测准确率在92.5%以上。     

Evolutionary RBF classifier for polarimetric SAR images

In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.     

板带材缺陷检测中的多特征优化组合方法研究

针对冷轧板带材常见表面缺陷图像识别的特点,提出了板带材表面缺陷多特征优化组合方法,该方法以直方图统计特征、小波变换特征、灰度共生矩阵特征、不变矩特征等4类特征共26维特征向量为基础,依据类间类内距离差的类别可分离性判据对特征进行优化,选出最优特征向量组合。对6类典型板带材表面缺陷进行实验,实验结果表明,采用多特征优化组合方法选择出的特征向量具有较好的分离效果,显著地提高了表面缺陷的识别率。     

基于独立成分分析和神经网络的高光谱遥感数据分类

高光谱遥感数据以数据量大、含混度高、地面样本数据少的特点给分类处理带来了困难。将独立成分分析技术与多层前向神经网络相结合, 得到一种新的分类算法。独立成分分析在提取有效光谱特征的同时, 大大降低了数据的维数。神经网络作为分类器, 分类精度显著高于传统的bayes 分类器。通过对220 波段的高光谱数据进行实验, 得到了良好的效果。     

Color image segmentation using feedforward neural networks with FCM

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features “a” and “b” of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.     

一种基于BP神经网络的代码相似性检测方法

如何有效地检测程序设计课程作业中的抄袭现象是一个重要的问题。传统的抄袭检测方法主要利用代码的属性或结构信息来度量代码之间的相似性。给出了一种基于误差反向传播(BP算法)多层前向神经网络的代码抄袭检测方法。提取程序之间的7种比较特征作为神经网络的输入,经过网络计算后得出程序的相似值,并将该值与抄袭决策阈值相比较以判定存在抄袭现象的程序集。实验结果表明,本方法具有很好的检测效果。     

基于香蕉雷达回波图像的特征提取与识别

为了解决高速公路绿色通道验货部门存在的难以用人工的方法去判断车中是否夹带违禁物的实际问题,在采集香蕉的雷达回波图像的基础上,利用灰度共生矩阵的特征提取,提出了基于反向传播(BP)神经网络对香蕉的雷达回波图像进行识别和分类的方法,编制了香蕉的雷达回波图像的识别和分类软件。通过在河南高速服务区绿色通道验货部门的实际应用,表明该软件有较好的识别和分类效果。     

A comparison of texture measures for the per-field classification of Mediterranean land cover

Land cover of a Mediterranean region was classified within an artificial neural network (ANN) on a per-field basis using Landsat Thematic Mapper (TM) imagery. In addition to spectral information, the classifier used geostatistical structure functions and texture measures extracted from the co-occurrence matrix. Geostatistical measures of texture resulted in a more accurate classification of Mediterranean land cover than statistics derived from the co-occurrence matrix. The primary advantage of geostatistical measures was their robustness over a wide range of land cover types, field sizes and forms of class mixing. Spectral information and the variogram (geostatistical texture measure) resulted in the highest overall classification accuracies.     

遥感图像林型纹理特征的ICA与SVM分类

遥感图像纹理特征是光谱相近林型准确分类的有效方法,然而其带来分类特征向量维数增加和计算量增大。因此,对南方山区林地TM图像进行独立成分分析ICA降维,通过计算灰度共生矩阵获取纹理特征,使用SVM分类,研究林地类型的快速分类方法。结果表明,ICA与SVM法利用遥感图像纹理特征可较准确地实现林地类型分类,分类总精度、Kappa系数分别为85.4%、0.73,均高于SVM法、BP神经网络法、最大似然法、最小距离法;其对阔叶林、针叶林、竹林的分类精度依次为78.2%、80.1%、84.3%,误识率主要是由于混交林而造成两类林地之间存在交集,易出现的针阔混交林使得阔叶林、针叶林的分类精度低于竹林。     

Entropy-based texture analysis in the spatial frequency domain

An approach which uses regional entropy measures in the spatial frequency domain for texture discrimination is presented. The measures provide texture discriminating information independent of that contained in the usual summed energy within based frequency domain features. Performance of the entropy features as measured by a between-to-within-class scatter criterion is comparable to that of traditional frequency domain features and gray level co-occurrence contrast features. A method of frequency scaling is introduced to enable the comparison of texture samples of different subimage size. The resulting regional entropy measures are subimage size-invariant subject to certain constraints which arise from properties of the discrete Fourier transform.