基于低对比度和小区域模型的快速二值化方法

针对在带钢检测过程中出现的低对比度和小区域特征的缺陷,设计了一种快速高效的图像二值化方法.同0TSU算法和灰度平均值算法比较,该图像二值化方法在具有小区域缺陷特征图像处理方面有一定的优势.该方法对图像进行二值化处理,速度快、精度高,适用于对速度要求较高的图像处理场合.     

镀锌带钢设备裂缝图像的分割技术研究

对镀锌带钢设备裂缝图像的优化分割处理,可以有效提高对设备裂缝的成因分析和修复的能力。提出一种基于图像细节增强和梯度方向纹理网格分割的镀锌带钢设备裂缝图像的分割方法。对目标图像进行细节增强处理,采用小波降噪方法进行图像滤波,提高镀锌带钢设备裂缝图像的纯度,沿裂缝梯度方法进行不规则网格分割,实现对裂缝图像分割算法改进。仿真结果表明,该算法能有效提高图像的峰值信噪比,图像分割的抗噪能力较强。     

基于图像预处理的神经网络带钢缺陷检测

针对带钢表面缺陷的特点,提出了1种基于图像预处理消除光照不均等的干扰且用神经网络进行缺陷识别的检测方法.带钢缺陷的检测分为3步:首先,对采集的图像进行预处理,通过图像零均值化以消除光照对检测的影响,分别利用维纳滤波和sobel算子对图像进行滤波除噪和锐化处理;其次,通过最大类间方差法进行图像分割以及计算面积来判断是否存在缺陷;最后,在提取图像特征的基础上,通过设计人工神经网络识别带钢缺陷类型.实验表明,采用的方法能够有效抑制图像背景干扰,能够有效地实现带钢缺陷的快速检测.     

基于ILLBP和ISSA的带钢表面缺陷识别

带钢表面缺陷存在着噪声、光照不均匀、纹理复杂以及局部区域对比度弱的问题。为此，提出了一种新的带钢表面缺陷识别算法。首先提出了ILLBP特征提取算法，在LBP算法中引入LTP的低阈值模式克服一定的噪声和光照影响，为了更好表征带钢表面缺陷复杂的纹理特征引入了ILBP中3种新的纹理结构，同时将LBP值的频率直方图改为了LBP局部梯度幅值和局部梯度方向的频率直方图，使其能更好地表征局部区域对比度强弱的关系；最后为了进一步提高带钢缺陷识别的准确率和减少冗余特征的影响，提出了一种改进的樽海鞘特征选择算法(ISSA)。在NEU数据集上仿真实验结果表明：算法(ILLBP+ISSA)能够克服光照不均匀、局部区域对比度弱、纹理复杂多样的影响，以及对噪声具有一定的鲁棒性。在高斯噪声信噪比为50 dB时带钢表面缺陷识别准确率能达到99.10%,40 dB时准确率能达到97.60%。     

基于图像零均值化的带钢缺陷检测

 针对带钢表面缺陷的特点,提出了一种基于图像零均值化的检测方法。首先,通过对测试图像进行零均值化,以消除光照对检测的影响;其次,利用维纳滤波对零均值化图像进行滤波除噪;在此基础上,采用Sobel进行锐化处理;最后,通过最大类间方差法进行图像分割,从而实现对带钢表面缺陷的检测。试验表明,本方法能够有效抑制图像背景干扰,有效地实现带钢缺陷的快速检测。     

热轧带钢在线测宽中的图像分割

对带钢质量检测中图像处理和分析的过程进行了论述.结合热轧带钢现场图像,应用最终测量精度准则对图像处理算法进行评价.实验评价结果表明,动态双阈值的图像分割效果最佳,适合应用于带宽实时在线检测.     

管道无损检测系统中基于色彩信息的焊缝识别方法

管道无损检测系统在色彩信息的焊缝识别方法使用过程中,如果所识别的图像对比度过低,在介绍管道无缝检测技术的基础上,分析管道无损检测系统焊缝识别的国内外研究现状,以及复杂背景下的管道焊缝识别原理,即如果所识别的图像对比度过低,就需要增加图像信息,建立相应类型的检测算法来实现图像缺陷的修复检测和图像目标的提取,因此进一步分析讨论图像处理的基本技术和彩色图像处理技术。     

基于线型激光的热轧带钢表面在线检测系统

针对基于面阵CCD摄像机的热轧带钢表面缺陷检测系统所存在的问题,提出采用线阵CCD摄像机作为热轧带钢的表面图像采集装置,将激光线光源作为照明光源,解决了高温环境下的远距离均匀照明问题.采用这种图像采集方式可以使系统的精度达到0.5mm以上.针对热轧带钢表面存在的氧化铁皮、水及光照不均等问题,提出了对于不同缺陷类型建立单独的缺陷检测算法的思想.目前已经建立了纵裂与边裂的检测算法,对这两类缺陷的检出率达95%以上.     

基于模糊逻辑的雾天图像增强改进算法

针对传统模糊增强算法的缺陷,提出了一种基于正弦函数变换的模糊增强算法.该算法首先对雾天图像进行规范化,降低不同雾况对图像灰度级分布范围的影响;其次通过一种改进的模糊化算法将图像从灰度域变换到模糊域,在模糊域内对低灰度区域进行衰减运算,对高灰度区域进行增强运算;最后将图像从模糊域变换到灰度域.试验表明,该算法可以有效提高雾天图像的对比度.     

基于FCM-WA联合算法的多种类矿石图像分割

矿石图像分割是基于机器视觉的矿石粒度分布检测的重要组成部分。针对复合矿山中颜色多样、纹理复杂且边缘粘连的多种类矿石图像难以识别与分割的问题,提出了一种基于FCM-WA联合算法的矿石图像分割方法。首先对矿石图像进行形态学优化,利用双边滤波、直方图均衡化和形态学重构来优化矿石图像的几何特征,减少噪声对分割效果的影响,提高图像对比度;然后将模糊C均值聚类（FCM）算法与分水岭（WA）算法相结合,利用FCM算法进行聚类迭代,计算出合适的分割阈值并对矿石图像进行分割,输出二值化图像;再利用基于距离变换的WA算法优化FCM算法的分割结果,对FCM算法输出的矿石图像边缘粘连部分进行分割,以获取最佳的分割图像。研究结果表明：（1）利用形态学优化流程处理矿石图像能够减少噪声并增强边缘信息,从而提高对比度;（2）相比传统的大津法和遗传算法,本文所提FCM-WA方法的稳健性更强、分割效果更好,对多种类的矿石图像像素分割准确率和矿石粒度识别准确率均可达到92%以上;（3）通过试验验证,FCM-WA方法能够精确地分割颜色多样、纹理特征复杂及边缘粘连的多种类矿石图像,分割结果满足粒度分布检测的要求;（4）FCM-...     

基于监督学习的前列腺MR/TRUS图像分割和配准方法

前列腺核磁超声图像配准融合有助于实现前列腺肿瘤的靶向穿刺。传统的配准方法主要是针对手动分割的前列腺核磁（Magnetic resonance, MR）和经直肠超声（Trans-rectal ultrasound, TRUS）图像上对应的生理特征点作为参考点,进行刚体或非刚体配准。针对超声图像因成像质量低导致手动分割配准效率低下的问题,提出一种基于监督学习的前列腺MR/TRUS图像自动分割方法,与术前核磁图像进行非刚体配准。首先,针对图像分割任务训练前列腺超声图像的活动表观模型（Active appearance model, AAM）,并基于随机森林建立边界驱动的数学模型,实现超声图像自动分割。接着,提取术前分割的核磁图像与自动分割的超声图像建立轮廓的形状特征矢量,进行特征匹配与图像配准。实验结果表明,本文方法能准确实现前列腺超声图像自动分割与配准融合,9组配准结果的戴斯相似性系数（Dice similarity coefficient, DSC）均大于0.98,同时尿道口处特征点的平均定位精度达1.64 mm,相比传统方法具有更高的配准精度。      

枝晶腐蚀低倍检验技术在连铸生产质量控制中的应用

简略介绍钢坯低倍硫印检验、热酸腐蚀、电解腐蚀、冷酸腐蚀和枝晶腐蚀技术的发展.详细介绍连铸坯最新低倍检验技术即枝晶腐蚀低倍检验技术的应用,包括检验流程、试样加工、腐蚀方法、评级方法,并结合部分钢产品图例分析,阐明了枝晶腐蚀低倍检验技术的优势,不仅能够清晰地显示连铸坯内部的凝固组织和各种缺陷,还能较准确地计算等轴晶率.该技术既能用于连铸坯内部凝固组织和缺陷的研究,还能用于铸坯生产实物质量的监控,为进一步提高钢材质量提供改进依据.     

Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment

Recent advances in optical sensors and computing technologies have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, and telecommunication conduits. It is now possible for inspection technologies that require no human entry into underground structures to be fully automated, from data acquisition to data analysis, and eventually to condition assessment. This paper describes the development of an automated data interpretation system for sanitary sewer pipelines. The interpretation system obtains optical data from the Sewer Scanner and Evaluation Technology (SSET), which is known to be the current leading-edge technology in inspecting sanitary sewer pipelines. The proposed system utilizes artificial neural networks to recognize various types of defects in sanitary sewer pipelines. The framework of this system includes modification of digital images for preprocessing, image feature segmentation, utilization of multiple neural networks for feature pattern recognition, and the fusion of multiple neural networks via the use of fuzzy logic systems.     

Image feature based automatic correction of low-frequency spatial intensity variations in MR images

An automatic method of compensating for low-frequency variations in magnetic resonance images is presented. Small variations within a tissue type are modelled and a correction function is generated. The method is based completely on image features and does not need a phantom or user interaction to generate the compensation function. This image correction simplifies digital image analysis and may enhance clinical evaluation. As a result, the correction technique reduces inhomogeneity and improves contrast. Our results show that the radiofrequency response variation of coils can be reduced. The segmentation process, even with a simple threshold method, produces more reliable results when corrected images are used. The presented method is most useful for images acquired in the sagital and coronal planes with circular local coils, or using surface coils, e.g., spine coils.     

冷轧带钢表面自动监测系统的研究

介绍一种新的基于图像处理的冷轧带钢表面自动监测系统。该系统采用面阵ＣＣＤ摄像头来采集带钢表面的图像,并且配备两种不同的照明方式,以检测不同类型的缺陷。系统在硬件结构和软件流程上进行了特殊的设计,以保证能够对带钢表面进行实时在线监测。同时,讨论了系统中采用的图像处理和模式识别方面的一些有效算法。用实际的样本对该系统进行试验,结果表明：该系统能识别六种常见的表面缺陷,识别率接近９０％。     

Systematic investigations of the contrast results of histochemical stainings of neurons and glial cells in the human brain by means of image analysis

The investigation of neurohistological specimens by image analysis has become an important tool in morphological neuroscience. The problems which arise during the processing of these images are non-trivial, especially if a pattern recognition of cells in the imaged tissue is intended. One of the major problems faced concerns the segmentation of structures of interest, whether cells or other histologic structures. The segmentation problem is often the result of an inappropriate staining procedure. For serious image analysis to be performed, the material under investigation must be optimally prepared. Spatially complex patterns, e.g. fuzzy-like neighbouring neurons, are easy to recognize for humans. But the integrative and associative performance of current artificial neuronal network schemes is too low to achieve the same recognition quality as humans do. Therefore, a general analysis of staining characteristics was performed, especially with respect to those stains which are relevant to object segmentation. Although most image analytical investigations of tissues are based on stained samples, a study of this type has not been previously conducted. Of the stains and procedures evaluated, the gallocyanin chrome alum combination staining provided the best stain contrast. Furthermore, this staining method shows sufficient constancy within different parts of the human brain. Even the fine nuclear textures are differentiable and can be used for further pattern recognition procedures.     

Effects of Solar Loading on Infrared Imaging of Subsurface Features in Concrete

This paper addresses the effect of solar loading on thermal imaging for the detection of subsurface deterioration in concrete bridge components. The deterioration of the concrete resulting from corrosion of embedded mild reinforcing and prestressing steel results in delamination and spalling that can affect the strength and serviceability of a concrete structure. The ability to detect this deterioration during its early stages, when mitigation efforts can be employed, can provide a useful tool for maintenance and inspection personnel. Infrared thermography is a tool that has the potential to detect these subsurface defects to compliment inspection efforts and provide an additional means of condition assessment for a bridge. However, the technique relies on thermal gradients developing in the concrete such that a temperature contrast exists between damaged concrete and sound concrete. The environmental conditions at the bridge, such as direct solar loading, ambient temperature variation, and wind affect the thermal gradient in the concrete, and hence, the ability to image subsurface features. The effects of direct solar loading on the detection of subsurface targets in a concrete test block have been studied. Quantitative measurements of the thermal contrast that appear in thermal images of the test block are reported and analyzed. The effect of the depth of the embedded target is discussed, as well as the timing of inspection (relative to sunrise) that resulted in maximum contrast in thermal images.     

基于多目标支持向量机的ADHD分类

针对结直肠息肉图像分割中病灶区域尺度变化大、边缘模糊以及息肉与正常组织对比度低等问题,导致病变区域分割精度低和分割边界存在伪影,提出一种基于Swin Transformer和图形推理的自适应网络. 该网络一是利用Swin Transformer编码器逐层提取输入图像的全局上下文信息,弱化背景信息干扰,多尺度分析病变区域的显著性特点. 二是提出全局与局部特征交互模块增强网络对复杂病灶的空间感知能力,突出待分割目标的关键位置信息,精准定位目标. 三是通过区域引导图推理模块以图循环递推的方式挖掘先验信息之间的高阶显性关系,促进图间信息传递. 四是设计面向边缘细节的边缘约束图推理模块,整合边缘细节,改善分割效果,提高分割精度. 在CVC-ClinicDB、Kvasir、CVC-ColonDB和ETIS数据集上进行实验,其Dice系数分别为0.939,0.926,0.810和0.788,平均交并比分别为0.889,0.879,0.731和0.710,分割性能优于现有方法. 仿真实验结果表明,对于形态结构复杂、对比度低和边缘模糊的结直肠息肉图像均有较高的分割精度.

     

直缝埋弧焊管用St52—3钢板缺陷分析

针对直缝埋弧焊管用St52—3钢板出现的异常情况,通过低倍、金相、扫面电镜等手段进行了全面分析,负偏析带内贝氏体组织和夹杂物导致钢板出现中心裂纹,从而出现质量缺陷。