基于图像识别的数字多用表自动校准系统开发

手持式数字多用型号规格多种多样,需校准的数据点非常多,人工校准过程耗时费力,且容易产生错误.本文提出了基于图像识别技术的数字多用表自动校准系统(以下简称系统)方案,系统根据被校准数字多用表的量程及最大允许误差指标等参数生成校准记录模版,对于每个校准点采用SCPI语句控制标准源的输出标准值,通过图像识别的方式读取校准结果...     

图像识别的数字万用表自动校准装置

数字万用表是大多数企业普遍常用的计量工具,常规传统校准一个数字万用表全部功能需花去0.5~1h。为了提高效率,节省时间和繁琐的重复工作,校准装置通过计算机来控制标准源的输出,用摄像头将数字万用表的数值图像采集到计算机中,用计算机进行图像数字识别,识别结果记录到Excel表格中,从而完成整个数字万用表的校准工作。     

归一化算法实现畸变不变图像识别研究

在光电混合联合变换相关器图像识别系统中,对与参考图像完全相同的真目标图像和不同的假目标图像能实现完全识别,但对于与参考图像间存在旋转和比例变化的目标图像却很难识别,甚至无法识别。由此,提出了基于图像质心和最长边的归一化图像处理新算法:根据图像像素总值求取图像质心,根据质心和图像边缘轮廓点之间的长度求出图像的最长边和旋转角度,然后根据统一标准将目标图像和参考图像调整至同一长度和旋转方向的完全相同的图像。最后给出了模拟和实验的相关结果,结果表明,该方法能实现存在旋转和比例变化的图像的畸变不变处理,从而提高了系统的识别能力。     

横滤嘴棒自动剔除和图像识别预警方法

目的 基于图像识别和结构改进对嘴棒发射机运行中产生的横滤嘴棒进行智能剔除、识别和预警,以提高生产效率。方法 采集滤嘴棒侧面运输图像,对异常图像进行追踪,从中提取异常滤嘴棒特征点,基于图像生成警示信息,同时对滤嘴棒发射机输送轨道背板进行结构优化,能剔除大部分横滤嘴棒。结果 改进后的模拟样机,经过输送轨道背板的横滤嘴棒的剔除率高达约77%,其余异常横滤嘴棒被及时预警,嘴棒发射机的管道卡堵概率降低约60%,设备运行效率显著提高。结论 图像识别和结构改进能有效识别预警和剔除嘴棒发射机运行中产生的横滤嘴棒。     

基于Python-opencv图像识别的手持式数字多用表自动检定方法

针对当下手持式数字多用表无通信接口,多数机构仍使用手动或者半自动校准的问题,提出一种基于Python-opencv图像识别的手持式数字多用表自动校准方法。通过摄像头拍摄图像,利用计算机Python-opencv程序图像识别技术进行数字识别,并将识别结果记录到计算机表格中进行后期结果处理,这大大减少校准时间和降低了出错率。     

图像识别黏度计自动检测系统的设计与实现

在计量领域中,传统的黏度计检测过程是依靠人眼观察、手动计时的方法来进行,受到人为操作误差影响较大,且检测效率低下。文章介绍了一种基于C8051F单片机的黏度计自动检测系统,利用图像处理技术实现了黏度计检测的自动化。     

图像识别技术在铁路监控系统中的应用

文章首先就铁路工作环境中监控系统以及图像识别技术的应用意义做出了必要说明,而后对当前应用领域中的图像识别技术原理和主流技术做出了相应阐述。     

基于图像识别的数字温湿度计自动校准装置的设计

本文通过对现有数字温湿度计校准装置及方法的综述,指出当前自动化校准需求的迫切性,并提出一种基于图像识别技术的数字温湿度计自动校准装置的设计方案。在现有校准装置的基础上,添加高分辨率工业相机、样品支架和条形光源等设备,通过LabVIEW进行自动校准系统开发,实现对多表位数字温湿度计显示值的拍摄,并自动进行图像处理、数据识别和生成原始记录等。通过该设计方案,在一定程度上有效地降低了人员劳动强度,大大提高了数字温湿度计的校准工作效率和测量准确性。     

高精度表面粗糙度图像识别系统的研制

本文研究的是一套通过干涉显微镜成像,利用CCD相机提取干涉条纹进行图像识别和处理,实现高精度工件表面粗糙度快速、非接触、自动化的检测系统。该系统在干涉显微镜的结构基础上,安装了CCD相机,将干涉图像采集到计算机中,然后对采集到的图像进行滤波、判别、二值化处理、提取边缘轮廓,并按照GB/T 1031-2009《产品几何技术规范(GPS)表面结构轮廓法表面粗糙度参数及其数值》的要求进行计算,得到被测工件的表面粗糙度。经实验验证,该检测系统满足Ra值(0.01~0.1)μm和Rz值(0.1~1.0)μm测量要求,适用于高精度工件的表面粗糙度测量。     

基于图像识别算法的图像传感器质量检测方法改善研究

针对图像传感器质量检测结果可信度低的问题，基于Laplacian算子图像识别技术，借助Matlab软件，建立图像识别模型，实现图像采集、图像边缘智能识别、图像特征信息智能提取与分析的功能。通过实验对比，证明改善后的检测方法能更准确地反映产品质量特性。     

Statistically optimised method for detecting adult image groups

Adult image recognition is an important technique for preventing children from accessing offensive material on the Internet. Most of the related works focus on single image recognition. However, adult images usually exist as a group and rarely stand alone. Therefore, considering the entire image group as a whole for classification should be more effective. This paper presents a new method of detecting adult image groups, which aims at achieving optimal recognition accuracy. Adult image group recognition generally includes two components: an adult image recogniser and a final decision rule for classifying the image group. We provide a theoretical analysis to clarify the correlation of the two components and use probability formulae to estimate the recognition rates for different settings of the adult image recogniser and the decision rule. Then, a set of optimal receiver-operating characteristic (ROC) curves for different image numbers is solved. To recognise an unknown image group, a desired recall rate for adult (or benign) image groups is specified and the system is set according to the parameters acquired from the optimal ROC curves. The proposed method can be dynamically adapted to the recall rates that the user expects. This advantage makes the proposed system more suitable for real applications. Our work can be viewed as an extension of single image recognition and the experimental results demonstrate that it can attain higher recognition accuracy than the earlier methods.     

基于神经网络的在线计算机图像识别系统

以二值型自适应共振理论（ART－1）神经网络为识别核心设计了一个应用于生产流水线的计算机识别系统，它可以对生产线上的零件和产品的文字和符号进行实时识别，作自动记录。该系统具有学习和识别速度快、识别率高（＞96％），可以灵活改变识别对象，应用范围广等特点。     

Cervical image classification based on image segmentation preprocessing and a CapsNet network model

Cervical cancer is one of the most common gynecological malignancies, and when detected and treated at an early stage, the cure rate is almost 100%. Colposcopy can be used to diagnose cervical lesions by direct observation of the surface of the cervix using microscopic biopsy and pathological examination, which can improve the diagnosis rate and ensure that patients receive fast and effective treatment. Digital colposcopy and automatic image analysis can reduce the work burden on doctors, improve work efficiency, and help healthcare institutions to make better treatment decisions in underdeveloped areas. The present study used a deep-learning model to classify the images of cervical lesions. Clinicians could determine patient treatment based on the type of cervix, which greatly improved the diagnostic efficiency and accuracy. The present study was divided into two parts. First, convolutional neural networks were used to segment the lesions in the cervical images; and second, a neural network model similar to CapsNet was used to identify and classify the cervical images. Finally, the training set accuracy of our model was 99%, the test set accuracy was 80.1%, it obtained better results than other classification methods, and it realized rapid classification and prediction of mass image data.     

色貌模型在跨媒体图像复制中的应用研究

目的 研究CIECAM02和iCAM色貌模型颜色空间的均匀性,以及应用于不同观察条件下的图像复制效果。方法 利用均匀颜色数据集测试色貌模型颜色空间的均匀性,并通过视觉评价实验来测试2个色貌模型的图像复制效果。结果 CIECAM02的色貌空间具有更好的色差均匀性,iCAM的色貌空间具有更好的色相、明度和彩度均匀性。在不同观察条件下的图像复制中,CIECAM02有更好的表现。结论 iCAM的颜色空间适合于颜色色貌属性的计算,而CIECAM02的颜色空间更适合于颜色色差的计算,在不同观察条件下的图像复制中,CIECAM02有更稳定的表现,但并不是对所有图像都有更好的表现。     

基于提升小波变换和分形维数的声纳图像识别

分形理论在图像的纹理识别中得到了广泛应用,由于分形维数不能反映图像的空间信息,容易造成误识别。针对该问题并结合声纳图像的特点,通过提升结构构造了Haar小波,并将提升小波变换同分形理论相结合,利用小波分解的多分辨率特点和分形维数的多尺度特性,提高图像的识别率。采用Levenberg-Marquardt(L-M)算法优化的BP神经网络对不同信噪比的声纳图像进行分类识别。实验结果表明,文中方法不论在识别率还是识别时间上均优于传统纹理识别方法。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

Combination of direct matching and collaborative representation for face recognition

Difficulties associated with the use of Buchdahl's retardation coefficients in image assessment are examined. It is shown that, by a series of approximations and corresponding transformations, the set of coordinates of transmitted rays from any object point can be expressed as a circular region perpendicular to the optical axis. Furthermore, it is shown that, under these transformations, the form of the retardation expansion remains constant and only the coefficients need be altered. These changes are independent of the field angle, but depend on the f-number of the system. The coefficients thus derived are field-independent in contrast to those specified by most authors. Expressions for the coefficients under each of the transformations introduced are presented. Also a brief discussion of the convergence of the retardation expansion is presented and the results indicate that the above approximations are sound over the region of convergence of the truncated aberration expansion of order eight.     

基于图像色貌模型的高动态影像显示技术研究

目的研究高动态影像(HDR)在低动态范围显示设备上的高保真显示问题。方法采用图像色貌模型(iCAM)设计高动态影像的再现技术框架,并使用C++语言搭建软件实现系统,通过计算机测试平台对输入图片进行了实证,算法重点是处理影像的明视度。结果显示图像的动态范围被拓展10%以上,色差小于1.6ΔE*ab,图像细节得到了加强,基本达到了高动态影像高保真显示的要求。结论可利用iCAM框架来实现高动态影像在常规显示设备上高保真显示的问题,具有重要的理论意义与技术价值。     

基于卷积神经网络图像分类的轴承故障模式识别

针对传统的基于数据驱动的机械故障模式识别方法中需要人工构造算法提取特征以及人工构造特征提取算法繁琐的问题,结合卷积神经网络(CNN)在图像特征自动提取与图像分类识别中的广泛应用,提出了一种基于CNN图像分类的轴承故障模式识别方法。首先,利用集合经验模态分解(EEMD)方法对轴承振动信号进行自适应分解并用相关系数对得到的本征模函数分量进行筛选。其次,对筛选得到的本征模函数分量进行伪魏格纳-威利时频分析(PWVD)计算得到信号的时频分布图,并对时频图进行预处理。最后,将轴承15种不同工况预处理后的时频图利用CNN进行特征提取与分类识别。将该方法与同类方法进行了对比,分类正确率提高了4.26%。     

基于图像分析技术的螺纹自动化测量系统研究

为提高紧固件螺纹检验效率,实现经济的螺纹全参数检验,通过工业相机和光源选型,标定方法设计搭建了螺纹图像摄取系统,通过算法设计和优化编制了配套软件,对图像分析技术应用于螺纹检测分析进行了研究,并对测量系统进行了不确定度评定。研究表明基于图像分析技术的螺纹自动化测量系统可实现。