Sectional Dimensions Identification of Metal Profile by Image Processing

In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.

3D surface profile diagnosis using digital image processing for laboratory use

The measurement of the surface quality and the profile preciseness is major issues in many industrial branches such that the surface quality of semi products directly affects the subsequent production steps.Although,there are many ways to obtain required data,the hardware necessary for the measurements such as 2D or 3D scanners,depending on the problem’s complexity,is too expensive.Therefore,in this paper,what we put forward as a novelty is an algorithm which is verified on the model of simple 3D scanner on the image processing basis with the resolution of 0.1 mm.There are many ways to scan surface profile;however,the image processing currently is the most trending topic in industry automation.Most importantly,in order to obtain surface images,standard high resolution reflex camera is used and thus the post processing could be realized with MatLab as the software environment.Therefore,this solution is an alternative to the expensive scanners,and single-purpose devices could be extended by many additional functions.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.

Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.

Elimination of Wind Wave Effects on Sub-Bottom Profiler Record

With the presence of wind waves, the swaying of survey vessel may effect the quality of sub-bottom profiler records and, therefore, it is necessary to correct the distortions induced by wave action. A major issue is to distinguish wind wave effect and real bedforms such as sand waves. In this paper, a bandstop filter is designed according to the frequency features of wind wave effect to treat the distortion of seabed topography by wind waves. The technique is used to correct the sub-bottom profile in order to eliminate the wave-induced distortions for the sub-bottom profile records from the Yangtze Estuary. This study shows that the undulate seabed record is resulted from wave action, rather than the presence of sand waves, and the filtration technique helps to eliminate the wave effect and recover the real morphology of seabed and the sediment sequence underneath. In addition, a method for data processing is proposed for the case that the record indeed represents a combination of wave effects and real bedforms.

Improving signal to noise ratio of seismic profiles using an image processing technique

This paper introduces an image processing technique into seismic data processing as a noise attenuation technology. The image separation of the seismic profile is obtained by using grating operators based on different time dips and a set of relative single dip profiles is obtained. A high signal to noise ratio profile can be obtained during reconstruction by statistical weighting. With further processing analysis and geological study, a high signal to noise profile that can meet geological requirements can be produced. The real data examples show that the signal to noise ratio of the profile is greatly improved, the resolution of the profile is maintained, and the fault terminations are much clearer after using the image processing method.

A novel method for eliminating rotation deviation in sequential images mosaic

To eliminate rotation deviation of sequential images mosaic when measuring linear dimensions of large scale parts with computer vision, a novel algorithm based on the chain code searching method is proposed. After image preprocessing, including image filtering, image segmentation, and edge detection, the chain code length of the contour line can be searched out by the proposed method. Then, the angle from the contour line to the coordinate axis is computed with the length of the contour line. After that, the sequence is rotated in the opposite direction and the rotation deviation is eliminated. It is prepared for the next mosaic of sequences in eliminating shifting deviation. Experiments are carried out on parts with a linear profile rotating angle from 0° to 9°. The results show that compared with the commonly used Hough transform, the new method has higher precision and faster speed, which is important in realizing online high precision measurements of large scale parts with a linear profile.

An Effective Method to Determine the Distribution Boundary of Cobalt-Rich Fe-Mn Crusts on a Guyot: Synchronous Application of Sub-bottom Profiling and Deep-Sea Video Recording

Determining the upper boundary of the cobalt-rich crust distribution of a guyot is important for estimating the mineral resources available, however, it has also long been an unsolved question. Correlations between the sub-bottom structures, revealed by sub-bottom profiling, and crust distribution can be revealed for the first time by the synchronous application of sub-bottom profiling and deep-sea video recording. The lower boundary of the sediment corresponds with the upper boundary of the crust. By analysis of these two kinds of data, the lower boundary of the sediment can be determined; therefore, the upper boundary of the crust distribution can be deduced. According to this method of analysis, the upper boundary of water depth of crust distribution of a seamount in the western Pacific is about 1 560 m.

Image Analysis of Fabric Pilling Based on Light Projection

The objective assessment of fabric pilling based on light projection and image analysis has been exploited recently.The device for capturing the cross-sectional images of the pilled fabrics with light projection is elaborated.The detection of the profile line and integration of the sequential cross-sectional pilled image are discussed.The threshold based on Gaussian model is recommended for pill segmentation.The results show that the installed system is capable of eliminating the interference with pill information from the fabric color and pattern.

2 kHz High Power Smart Transducer for Acoustic Sub-bottom Profiling Applications

In this study,a 2kHz Tonpilz projector was designed using a Terfenol-D and modeled in ATILA.For the purpose of modeling studies,it has been determined that a radiating head mass exhibits better transmitting current response（TCR） at 136 mm diameter,where the resonance occurs at 2.4kHz and the peak value of 118 dB re 1 μPa/A at 1 m occurs at 12 kHz.Also bolt at a 46mm distance from the center of the head mass offers resonance at 2.4kHz,and the peak value of 115.3 dB re 1 μPa/A at 1m occurs at 11.5kHz.This optimized design is fabricated and molded with polyurethane of 3mm thickness.The prototype was tested at the Acoustic Test Facility（ATF） of National Institute of Ocean Technology（NIOT） for its underwater performances.Based on the result,the fundamental resonance was determined to be 2.18kHz and the peak value of TCR of 182 dB re 1 μPa/A at 1m occurs at 14 kHz.The maximum value of the RS was found to be -190 dB re 1V/μPa at 1m at a frequency of 2.1kHz.

ESR Projection Profile Imaging

In the present paper we report an ESR imaging algorithm and program based on the projection profile technique, by which the stereograrns and their cross-sectional contour maps of sample spin density can be plotted. As an example, we give some figures in order to demonstrate the applicability and reliability of the program. Keywords ESR imaging, Projection profile principle, Sampling process, Smooth surface, Function reconstruction, Stereogram

FY-4A数据在2021年1月6—8日寒潮监测中应用

基于静止气象卫星图像解译理论和位涡理论,应用FY-4A水汽图像、干涉式大气垂直探测仪(GIIRS)产品和欧洲中心第五代大气再分析资料(ERA5)对2021年1月6—8日我国中东部地区寒潮天气进行分析。结果表明:高空冷涡和地面冷高压是影响此次寒潮天气的主要影响系统,冷空气5日08:00从贝加尔湖南部向东、向南加强发展,8日20:00减弱;对比FY-4A/GIIRS与ERA5850 hPa 24 h变温数据发现:两种数据反映的对流层低层冷空气东移南下特征和正负变温区域分布特征基本一致,FY-4A/GIIRS 850 hPa 24 h负变温中心和0℃等变温线的位置与ERA5数据接近,FY-4A/GIIRS 850 hPa 24 h变温中心值强度略大于ERA5。水汽图像暗区变暗附近的高层位涡的增加激发高层气旋性环流后侧下沉运动增强,干空气下沉造成了地面高压增强;500 hPa冷涡中心附近绝对涡度增加是引起冷空气增强的原因之一;冷空气急剧增强时段,中低层位涡的加大引起地面冷空气堆积,寒潮增强。FY-4A/GIIRS温度产品反映的冷空气移动特征与位涡理论对冷空气增强原因分析结果一致,说明应用FY-4A/GIIRS温度产品能够有效监测分析寒潮冷空气演变特征。

双圆极化近场成像张量全息超表面

近场波束赋形可用于近场通信、无线功率传输等领域,而基于全息超表面设计的近场成像天线具有剖面低、无需馈电网络和波束调控灵活等优势。对标量和张量全息超表面近场成像进行了理论推导、仿真分析和实物验证。在此基础上,利用张量全息超表面设计了一款集成平面馈源的近场成像天线,全波仿真和测试结果相吻合,验证了该超表面具有可同时利用左旋和右旋圆极化进行近场成像的能力。

基于无透镜傅里叶变换数字全息的结构形貌测量

为了精确测量物体的形貌信息,提出了一种基于无透镜傅里叶变换数字全息的成像系统。首先,分析了无透镜傅里叶变换数字全息的成像原理,引入了倾斜平面校正方法;然后,搭建了成像系统平台,通过实验验证系统的成像性能。实验结果表明:系统的实际分辨率为57.02 lp/mm。测量结果的相对误差仅为3.95%,充分验证了该系统卓越的成像性能。

基于K-means聚类和图像分割的紫色土发生层边界识别

土壤学始于对土壤剖面及其形态特征的观察,剖面发生层的划分与发生层边界特征的描述是土壤调查的基础。实地划分发生层需要丰富的土壤学实践经验,存在主观和缺乏统一划分标准的问题。以紫色土剖面图像为研究对象,采用K-means聚类和图像分割技术,结合图像的颜色特征(CIELab色彩空间)和纹理特征(Entropy)识别紫色土剖面发生层边界,并与实地划分的结果进行比较。结果表明:(1)CIELab色彩空间的a、b通道和Entropy纹理特征,可以划分出供试剖面的主要发生层(A、B、C)和基岩(R);(2)聚类识别的发生层数量和发生层深度与实地识别的结果基本一致;除Z2剖面的C层和Z6剖面的Ap层聚类识别与实地识别的发生层下边界深度相差较大(分别为13cm和8cm)外,其余发生层下边界深度相差均在3 cm以内;(3)聚类识别的发生层边界形状更为不规则,明显度更为模糊。K-means聚类和图像分割技术实现了紫色土剖面发生层边界的客观识别,可为土壤剖面智能辨识系统的开发提供科学参考。

成形砂轮激光修整的多轮廓图像合成检测方法

为了解决复杂轮廓成形砂轮激光修整过程中在线检测难、轮廓检测精度低、速度慢等问题,搭建了一套基于砂轮激光修整机床的图像采集系统,提出了一种多轮廓图像合成检测方法,定义了轮廓波动带的概念。首先利用引导滤波和灰度变换预处理砂轮轮廓图像,增强轮廓特征;再使用全局阈值分割和反二值化得到二值图像,将二值图像序列合成为带有像素频率信息的轮廓频率图像,通过阈值分割提取出轮廓波动带;最后使用随机抽样一致算法结合最小二乘法对波动带最外侧的边缘点集进行拟合,完成直线和圆弧特征的检测。结果表明,该检测方法与单一截面视觉检测法相比,检测效率接近,但误差范围缩小了66.67%;与磨削复映检测法相比,检测结果存在一个固定偏差,平均误差在7μm左右,但检测效率提高了91.67%。该方法能高精度、高效地检测成形砂轮轮廓,为成形砂轮轮廓检测方法提供了新思路。

柴达木盆地西北缘尖顶山和黑梁子背斜的几何学与运动学特征分析

选取青藏高原北部柴达木盆地内尖顶山和黑梁子背斜作为研究对象,基于遥感影像和地震剖面,解释并分析该背斜构造带的地表次级断裂以及地下构造的变形样式与运动特征。遥感影像显示,尖顶山背斜的大部分地表次级断裂从北西走向的压扭断裂向东南方向过渡为南北走向的正断层,而黑梁子背斜的地表次级断裂为北北东走向的左行张扭断层。地震剖面显示,尖顶山和黑梁子背斜由上部逆冲滑脱断裂和下部基底正花状构造组成,深层与浅层的变形解耦主要受新生界下部路乐河组的膏盐层影响。结合地表次级断裂和地下基底断裂的平面展布样式、运动特征以及沿走向的变化,认为尖顶山和黑梁子背斜为右行压扭构造,推测在印度板块与欧亚大陆汇聚的背景下,柴达木盆地内部在持续缩短变形的过程中发生向东的差异性挤出,从而形成一系列压扭性质的背斜构造。

多模态MR功能成像参数对乳腺癌患者术前辅助化疗反应性的评估价值

目的对乳腺癌患者在手术前进行辅助化疗时,利用扩散加权成像(DWI)与动态增强MR成像的定量参数进行评估的价值进行了深入的研究。方法在2020年1月至2022年10月期间,挑选了43名在医院接受治疗的乳腺癌患者作为研究样本,患者都接受了扩散加权成像(DWI)和多模态MR功能成像参数诊断。同时,也采集了两种MR的功能性图像参数,然后对这些参数的变化情况进行了详细的分析,最后评估了其在病理学上的诊断准确性。结果LD值的比较结果表明,两组间并未存在明显的区别,P>0.05;然而,MHR组的ADC、E、Emax值超过NMHR组,P<0.05。通过以手术后的病理反应作为“金标准”,来描绘ROC曲线图,发现乳腺癌的各项指标的诊断准确度都很高。通过二元Logistic回归分析,发现LD、ADC、E、Emax值和乳腺癌患者手术前的辅助化疗反应之间存在着积极的联系。这表明它和乳腺癌患者在手术前的辅助化疗效果有着紧密的联系。结论对于乳腺癌患者,手术前的辅助化疗的效果可以通过结合扩散加权成像(DWI)和动态增强MR成像的定量指标来得到准确的评价,这将对接下来的医学治疗工作产生重要的支持。

玉兔二号巡视器影像DEM撞击坑中心点修正研究

针对巡视器重建DEM影像人工识别撞击坑中心点位置繁琐且易出错的问题,提出了一种撞击坑中心点自动提取和修正的算法。算法应用三次样条插值和滑动指针法提取8个坑唇点信息,再利用最优化代价函数进行圆拟合,并通过对中心点进行迭代更新得到较真正的DEM中的撞击坑中心点。通过与人工提取的检校识别结果对比表明,96%的撞击坑中心得到了有效修正。剖面线与深径比抽样结果进一步证明,97.81%的撞击坑剖面线提取检校结果一致。总体而言,该撞击坑数据中心点自动校正方法大大减少了人工识别的工作量,提高了撞击坑中心点提取的准确性,为区域小尺寸撞击坑形态参数自动提取提供了关键基础。

复杂型面结构超声成像检测研究进展

工程结构普遍具有复杂型面,而且此类区域是制造和服役阶段损伤甚至失效的高发区。超声无损检测是评估结构制造质量和服役安全性的重要手段,但复杂型面给超声检测带来了声耦合困难、随形扫描控制难、超声入射/接收难、超声传播行为复杂等挑战。本文分别从超声耦合策略、超声换能器、随形扫描控制、复杂型面轮廓重建、成像算法、复杂型面与材料复杂耦合的影响几大方面,概述了现有研究已取得的重要进展。最后探讨了复杂型面超声成像检测研究仍面临的挑战以及未来发展趋势,包括超复杂型面三维异形结构检测、高柔性二维阵列超声换能器、以及同时考虑宏/微观结构和材料自身声学特性的影响改进超声成像算法。