基于图像处理的织物缝纫平整度客观评价

织物的缝纫平整度是决定服装外观的重要因素,但肉眼评价存在一定的主观性,且常用的AATCC Test Method 88B-2006标准样照仅分5个等级,限制了评价的精度。利用图像处理技术探讨了客观评价织物缝纫平整度的方法。试验对5种常见织物采用不同的抽褶量进行车缝,以产生不同的平整度外观,进行平整度主观评价后,再借助MATLAB图像处理技术,提取缝纫图像的多个统计参数与小波特征,对客观参数与主观评价结果进行相关分析,得到与主观评价结果相关性较好的客观参数。研究结果表明:小波分解5层时的水平细节系数标准差,即SH5与主观评价具有良好的一致性,可以作为取代主观评价的客观指标。     

Using Wavelet Textural Features of Visible and Near Infrared Hyperspectral Image to Differentiate Between Fresh and Frozen–Thawed Pork

In this study, wavelet textural analysis was applied to hyperspectral images in the visible and near-infrared (VIS/NIR) region (400–1,000 nm) for differentiation between fresh and frozen–thawed pork. The spectral data of acquired hyperspectral images were analyzed using partial least squares (PLS) regression and five wavelengths (462, 488, 611, 629, and 678 nm) were selected as the feature wavelengths by the regression coefficients from the PLS model. The fourth-order daubechies wavelet (“db4”) was used to serve as the wavelet mother function for wavelet textural extraction of the feature images at the above selected feature wavelengths with the wavelet decomposition level from 1 to 4. Four textural features were calculated in the horizontal, vertical, and diagonal orientations at each level. Forty-eight textural features were extracted from each feature image and used to differentiate between fresh and frozen–thawed pork samples by least-squares support vector machine (LS-SVM) model. Wavelet texture extracted from all five feature images at first decomposition level was identified as optimal wavelet texture combination, resulting in the highest classification accuracy for the LS-SVM models (98.48 % for the training set and 93.18 % for the testing set). Based on the texture combination, the quality attributes of pork meat could be predicted with correlation coefficients of calibration (r _c ) of 0.982 and 0.913, and correlation coefficients of prediction (r _p ) of 0.845 and 0.711 for pH and thawing loss, respectively. The results showed the possibility of developing a fast and reliable hyperspectral system for discrimination between fresh and frozen–thawed pork samples based on wavelet texture in the VIS/NIR wavelength range.     

基于三维生物散斑技术的牛肉质构特性预测

三维成像技术能够获得样品的空间信息,具有快速、方便、可实时的特点。为了缩短生物散斑技术的检测时间,减小其应用的局限性,将三维成像技术引入到传统生物散斑技术中,以期得到更好的预测效果。分别从两个不同角度拍摄同一样品的图像信息,利用广义差分法对图像进行预处理,并运用灰度模板匹配法、小波变换法和对比度调制融合法对两个角度的图像进行匹配融合,以时间序列散斑图灰度共生矩阵的参数对比度表示图像的散斑活性,建立其对牛肉质构特性的预测模型。通过传统相机标定法,获得相机的内外参数,并利用相似三角形原理提取图像的深度信息,对因物体摆放位置不同引起的误差进行校正,使获得的结果更加准确。结果表明,相似三角形原理可对样品的深度信息进行校正。三维生物散斑技术能更好地对牛肉的硬度和咀嚼性进行预测,对3种图像融合方法进行比较可知,小波变换法的预测效果最好,对硬度和咀嚼性的预测相关系数分别可达到0.944 4和0.928 8。     

织物褶皱多尺度灰度共生矩阵分析

为建立一套客观、稳定、高效的织物褶皱评价系统,文章提出一种多尺度图像特征提取方法。首先,通过3层小波变换对织物二维图像进行分解,得到其高频系数;对原图及三个尺度下的小波系数分别生成灰度共生矩阵,并对这四个尺度的灰度共生矩阵提取对比度、相关性、角二阶矩、同质性及熵值表征织物褶皱变化;最后通过支持向量机对输入特征进行分类。结果表明,结合小波变换的灰度共生矩阵方法比单独使用灰度共生矩阵分类准确率高,说明多尺度的图像特征能够更加全面地描述织物褶皱变化。     

采用图像处理的织物缝纫平整度自动评估

为解决织物缝纫平整度客观自动评估时分类正确率低的问题,提出了一种基于灰度共生矩阵、小波分析和反向传播（BP）神经网络相结合的织物缝纫平整度的自动评估方法。首先采集标准缝纫图像,将图像的灰度级降至16 级,计算图像在0°和90°方向上的灰度共生矩阵并将其归一化,提取灰度共生矩阵的能量、熵、对比度和相关性4 个特征参数,并分别对特征参数在0°和90°方向上取均值;同时,运用Haar 小波在第6个分析尺度上提取并计算图像的水平细节系数的标准差。然后将提取的这5 个特征参数输入到BP 神经网络中训练和识别,并对标准缝纫图像进行了评估。评估结果显示：提出的算法与单独采用灰度共生矩阵特征、小波特征相比,具有较高的分类正确率,分类效果稳定。     

应用粗糙集和支持向量机的熔喷非织造布过滤性能预测

为预测熔喷非织造布的过滤性能,提出基于属性约简和支持向量机的预测方法。运用粗糙集理论在ROSETTA 环境下对含有9 个参数的熔喷非织造纤网结构参数全集进行约简,得到6 个各含3 个参数的约简集。分别将参数全集及各个约简集作为输入建立基于支持向量机（SVM）和ＢＰ 神经网络（BP-ANN）的28 个过滤性能预测模型,运用交叉验证法进行模型结构参数优化。结果表明：以含厚度、纤维直径和孔径的约简集为输入,基于ＳＶＭ模型预测准确度最高;其对过滤效率和过滤阻力的预测精度均超过98%,且CV 值均小于2%,表明这3 个参数是影响熔喷非织造布过滤性能的核心要素;基于SVM 模型的预测准确度总体优于基于BP-ANN模型的。     

Adaptive detection of weft‐knitted fabric defects based on machine vision system

This paper describes a machine vision system for the detection of weft‐knitted fabric defects based on an adaptive pulse‐coupled neural network (PCNN) and Ridgelet transform. In order to classify defects according to their different texture features, two methods are implemented: an improved PCNN method to segment the defects such as hole and dropped stitch from background image and a Ridgelet transform method based on wavelet analysis to identify the defect such as course mark. In implementing the PCNN model, necessary parameters of PCNN model such as linking coefficient, connection weight, and iteration number are automatically calculated in accordance with the spatial distance of neurons, mean, and variance value of whole image, and the cross‐entropy criterion. The function of Ridgelet transform is to identify the straight line marks and fit the regression equation for simulating the course mark in the image. The Ridgelet transform model can be simplified as the combination of Radon and wavelet transforms. The parameters of detected line are acquired by wavelet analysis in Fourier semicircle region. The experiment materials were several plain and interlocked weft‐knitted fabrics with hole, dropped stitch, and course mark defects. The fabric images were captured by an area‐scan camera with a resolution of 600 × 800 pixels, and signal processing was controlled by a digital signal processing multiprocessor on the inspection machine. The validation tests proved that the system performed well.     

基于小波变换的农田害虫图像预处理研究

以农田害虫识别系统中图像的预处理为研究对象,利用小波变换对图像进行不同尺度的小波分解,对得到的小波系数进行不同的处理,包括小尺度下的高频系数置零、阈值处理、模极大值处理以及增加大尺度下高频系数的相对值等方法,从而达到去噪、增强等图像预处理的目的.结果表明:利用小波变换对图像进行处理,可以收到良好的效果.     

基于小波与模糊相融合的苹果分级算法

为了准确获取苹果图像的边缘,实现苹果自动分级,提出一种基于小波与模糊相融合的苹果分级算法。对苹果图像进行全向小波变化,经模糊算法处理,通过自适应阈值,提取出苹果图像的边缘,再利用漫水填充算法,获取苹果图像的面积,根据苹果类圆特性,将面积转换为直径,并根据直径大小,完成苹果分级。仿真试验结果表明,该算法对3个级别苹果的分级正确率均在98%以上,说明该算法能够用于苹果的分级检测。     

Identification of Seeds by Colour Imaging: Comparison of Discriminant Analysis and Artificial Neural Network

This study describes the use of colour image analysis to identify four seed varieties. A wide range of kernel measurements was obtained from digitised colour images of whole seed samples of rumex, wild oat, lucerne and vetch. The combination size, shape (including kernel seven invariant moments) and texture parameters is the major element in this investigation. Two pattern recognition approaches were attempted in the classification: stepwise discriminant analysis, which is part of statistical pattern recognition techniques, and artificial neural network. The artificial neural network was found to outperform discriminant analysis. With only three inputs, a simple three-layer perception network exhibited performances exceeding 99% both in learning and test sets. It is shown that a mixture of features improved classification from 92% for size and shape parameters to 99% for size, shape and texture parameters. Two species, totally overlapped in the morphometrical space, were well separated by texture. The best characteristics are extracted from the red channel images. Limitations of neural computing concepts are discussed with respect to seed classification.     

结构参数对非织造材料吸声系数的影响

分别以涤纶针刺法非织造材料和丙纶熔喷非织造材料为基,通过热粘合方式制成不同参数的同质非织造材料吸声体。通过分析同质非织造材料吸声体的厚度、面密度、孔径、孔隙率等结构参数与其平均吸声系数之间的关系,探讨非织造材料结构参数对其吸声性能的影响。实验结果表明,对于同种材料而言,涤纶针刺非织造材料与丙纶熔喷非织造材料的结构参数对其平均吸声系数具有较大影响,材料的平均吸声系数随厚度和面密度的增加而增加。     

Quality grade identification of green tea using E-nose by CA and ANN

In this paper, the electronic nose (E-nose) is employed for the rapid identification of quality grades of green tea. Back-propagation neural network (BPNN) and probabilistic neural network (PNN) are two artificial neural networks (ANN) that are used widely. Then BPNN, PNN and cluster analysis (CA) are applied to identify the different tea samples. The first three principal components obtained by principal component analysis (PCA) are extracted as the inputs to the BPNN and PNN. Results of CA show that the classification of the different tea samples is possible using the response signals of the E-nose. Better experimental results are obtained using BP neural network. For the two neural networks BPNN and PNN, the classification success rates are 100% and 98.7% for the training set, respectively and these are, respectively, 88% and 85.3% for the testing sets. The overall results show that the two neural networks can be employed for identification of the different five tea samples.     

Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network

A rapid and cost-effective technique for identification of microorganisms was explored using fluorescence microscopy and image analysis, and classification was done with trained neural network. The microorganisms used in this study are Bacillus thuringiensis (C399), Escherichia coli K12 (ATCC 10798), Lactobacillus brevis (LJH240), Listeria innocua (C366), and Staphylococcus epidermis (LJH343). After staining the microorganisms with fluorescent dyes [diamidino-2-phenyl-indole and acridine orange (AO)], images of the microorganisms were captured using a digital camera attached to a light microscope. Geometrical, optical, and textural features were extracted from the images using image analysis. Parameters extracted from images of microorganisms stained with AO gave better results for classification of the microorganisms. From these parameters, the best identification parameters that could classify the microorganisms with higher accuracy were selected using a probabilistic neural network (PNN). PNN was then used to classify the microorganisms with a 100% accuracy using nine identification parameters. These parameters are: 45° run length non-uniformity, width, shape factor, horizontal run length non-uniformity, mean gray level intensity, ten percentile values of the gray level histogram, 99 percentile values of the gray level histogram, sum entropy, and entropy. When the five microorganisms were mixed together then, also the PNN could classify the microorganisms with 100% accuracy using these nine parameters.     

基于高光谱技术的铁观音茶叶等级判别

应用高光谱技术结合支持向量机分类理论对不同等级的铁观音茶叶进行判别分析。采集铁观音各等级茶样的高光谱数据,提取红边幅值、蓝边位置、黄边面积、红谷反射率、归一化植被指数等共20 个光谱特征参数,以其作为输入量带入以径向基函数作为核函数的支持向量机分类模型,探讨惩罚参数C和核参数g的最佳取值,构建判别模型,并对其进行验证和评价。结果显示,当惩罚参数C和核参数g分别为106和0.007 5时,所建模型对未知等级的铁观音样品正确判别率可达92.86%,表明应用高光谱技术进行铁观音茶叶等级的快速无损准确鉴别是可行的。     

基于机器视觉的淡水鱼去鳞损伤无损检测识别方法

为了研究淡水鱼去鳞损伤无损检识别方法,以鲤鱼为研究对象,搭建视觉采集系统,实现图像采集与处理。通过采集鲤鱼去鳞后有去鳞损伤的图像,将样本图像划分为鱼腹、鱼背、鱼尾、损伤、背景五部分,提取感兴趣区域(ROI),比较其颜色差异,发现正常鱼体的表面区域与损伤区域颜色差异较大,以不同区域对应的红色通道值(R)、绿色通道值(G)、蓝色通道值(B)对应像素的平均值作为输入,建立基于广义神经网络(GRNN)和径向基函数神经网络(RBF)的鱼体损伤区域检测识别模型,经测试,识别准确率分别能达到98%和80%。通过计算预测损伤区域与实际损伤区域的相关面积大小,验证识别模型的准确度,经试验验证,基于GRNN和RBF网络识别模型对去鳞损伤预测的准确率分别达到了90%和83%。研究结果表明,机器视觉可应用于淡水鱼去鳞损伤的无损检测识别。     

Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method

This paper proposed an approach, which is based on multi-scale wavelet transform and Gaussian mixture model, to solve the problem about automated fabric defect detection and improve the quality of fabric in the production. Firstly, the sample image was tackled by the “Pyramid” wavelet decomposition algorithm, and the new images were obtained by reconstructing with the produced wavelet coefficients using wavelet thresholding denoising method. Secondly, the obtained new images were segmented by applying the Gaussian mixture model that was based on the Expectation–Maximization (EM) algorithm. Various fabric samples were used in the evaluation, and the experimental results showed that the designed algorithm could precisely locate the position of defect and segment the defect.     

木棉及其混合纤维非织造材料吸声预测模型

以Allard模型为基础,采用等效流体的方法,拟建立一个能够准确描述木棉及其混合纤维非织造材料吸声行为的吸声模型。该模型结合木棉纤维的大中空结构,考虑了材料的热传导效应及纤维框架的柔软性。使用新建立的吸声模型及Allard模型分别对4种不同结构的木棉纤维非织造材料的吸声系数及比表面阻抗进行了计算,并将计算值与实验样品的测量值进行比较。结果表明:新模型的计算结果更接近实验值,说明新模型更适用于木棉及其混合纤维非织造材料吸声性能的预测,为工业产品的设计提供了理论参考。     

Discriminating and elimination of damaged soybean seeds based on image characteristics

To identify and eliminate damaged soybean seeds, images of Kaiyu 857 soybean seeds including those with insect damage, mildew, and other defects were acquired with an intelligent camera. After splitting the kernels from the background through using the data fusion, morphological corrosion expansion and a series of image processing algorithms, we extracted eight shape features, three color features and three texture features as the input layer to set up a BP neural network classification model with an average recognition accuracy of 97.25%. The identifying and eliminating device was tested five times with a mixture of 1000 differently damaged soybeans of seeds. The average accuracy rates of identification and elimination for normal, mildewed, insect-damaged, skin-damaged, broken and partly defective kernels reached 99.24%, 98.2%, 96.4%, 85.6%, 92.4% and 85.2% respectively. The efficient processing speed of the device reached 125 grains per minute. The results are of significance for the development of precise selection systems for soybeans or other crop seeds.     

缝纫平整度客观评判模型的研究

借助于图像处理技术,提取缝纫平整度照片的图像灰度标准差、图像熵、小波变换系数标准差、小波信息熵等特征参数,建立了缝纫平整度的客观评判的概率神经网络模型.经过训练和检验,得出该模型的预测值与期望值之间的相关系数在0.99以上,说明网络模型有效,且精度高,可以用于预测未知缝纫样本的缝纫平整度等级.     

基于扩展Shearlet变换、Krawtchouk矩和SVM的储粮害虫分类

为了进一步提高储粮害虫的识别精度,以便更有效地防治储粮害虫,提出了一种基于纹理和形状综合特征及全局混沌蜂群优化支持向量机(SVM)的储粮害虫分类方法。首先对储粮害虫图像进行扩展Shearlet变换,利用变换系数得到能量分布均值,加权后的能量分布均值构成纹理特征向量,用Krwtchouk矩不变量描述储粮害虫的形状特征;然后将纹理特征向量和形状特征向量分别归一化,两者结合构成储粮害虫的综合特征向量;最后用全局混沌蜂群算法优化SVM的核参数与惩罚因子,并应用参数优化的SVM进行分类。结果表明:与基于Gabor小波和支持向量机方法、基于Krawtchouk不变矩和支持向量机方法相比,本方法提取的储粮害虫特征信息更加完整,识别率更高。