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1.
一种基于Matlab的干涉条纹自动处理方法   总被引:1,自引:0,他引:1  
对接触式干涉仪量块检定时产生的两种干涉条纹进行了研究,图像整体采用傅里叶级数曲线拟合法,实现干涉暗条纹大致定位.局部采用最小二乘二次曲线拟合法,确定干涉暗条纹中心的精确位置及暗条纹个数.利用Matlab和VC平台进行编程,快速有效地实现了干涉条纹中心位置的自动判读. 该方法已应用在中国计量科学研究院接触式干涉仪的量块检定中.  相似文献   

2.
希尔伯特变换实时全息干涉条纹相位提取   总被引:2,自引:0,他引:2  
实时全息干涉法可以观察记录整个测试过程中条纹图的动态变化,传统相位提取算法只适合于静态干涉条纹图相位的提取.根据实时全息干涉条纹和希尔伯特变换的特点,提出了利用希尔伯特变换提取实时全息干涉条纹相位值的方法,采用了高通滤波的方法减少背景光强的影响,对铝片受力变形实验中实时全息干涉条纹的相位变化分布进行了提取.实验表明:希尔伯特变换法适合于动态条纹的相位提取,可以自动提取实时全息干涉测量过程中全场各点在任意两个时刻间的相位变化值,且测量结果与实时全息干涉条纹人工分析结果一致.  相似文献   

3.
为了提高对瞬态温度检测的灵敏度,提出了基于散斑干涉条纹光谱分析的瞬态温度反演算法.系统利用散斑干涉形成干涉条纹,由于瞬态温度的变化会使材料应变,从而使散斑干涉条纹改变.被测表面形变前后获得的干涉条纹由面阵 CCD 采集,其对应的光谱密度分布函数也会发生相应的改变,即由散斑干涉条纹反演得到的中心波长振幅发生改变.通过对两次中心波长幅值的比值的检测和计算,即可获得被测的瞬态温度.在分析计算了瞬态温度变化与材料应变、材料应变与干涉条纹变化的函数关系的基础上,推导了瞬态温度变化与干涉条纹振幅及相位函数关系.实验采用660 nm 半导体激光器,SI6600型面阵 CCD 探测器,从获得的光谱分布函数中提取中心波长处幅值比值,通过计算和标定,最终温度检测精度可达到±2℃.相比传统的直接检测干涉条纹的变化量,由被测面形变量推导温度的方法精度提高了近一个数量级,其精度更高、检测均匀性更好、稳定性更好.  相似文献   

4.
对高精度干涉条纹小数测量的方法进行了系统的介绍.就干涉条纹小数的定义、暗条纹中心的确定、量块中心长度测量的起始和终了位置进行了较全面的分析,实现了不确定度为2 nm的干涉条纹小数的测量.引用实验与国际比对结果理论分析予以验证.  相似文献   

5.
为了消除环境因素(尤其是振动和温度波动)在物体表面三维形貌测量中的影响,基于正弦相位调制(SPM)发展了一种光纤干涉条纹相位稳定技术。利用马赫-泽德光纤干涉仪结构和杨氏双孔干涉原理实现高密度的余弦分布干涉条纹投射。利用两光纤干涉臂端面的菲涅尔反射生成迈克尔逊干涉信号,由光电探测器(PD)检测后送入相位控制系统。采用相位生成载波的方法提取干涉信号的相位,并将生成的补偿信号闭环反馈给压电陶瓷驱动器,与正弦相位调制信号相加后共同驱动压电陶瓷,补偿环境因素带来的相位漂移,实现干涉条纹相位的稳定。环境因素对条纹相位的影响低于57 mrad,实验结果验证了该方法可行性。  相似文献   

6.
干涉图条纹数据的快速自动采集   总被引:1,自引:0,他引:1  
张晓  王晓辉 《光电工程》1997,24(6):54-59
干涉图条纹的数据采集是干涉图数据处理的前提和基础,提出了一种有效的从数字化干涉图中由计算机自动采集干涉条纹数据的算法,该算法的主要特点是首先用尺度验证技术对灰值干涉图进行干涉条纹细化,然后利用细化后的二值干涉图对干涉条纹进行条纹跟踪和数据采集。  相似文献   

7.
在相移算法的点衍射干涉术的基础上,提出了基于傅里叶变换的点衍射干涉测量术.基于傅里叶变换的点衍射干涉测量术是一种建立在点光源衍射干涉理论基础上的测量方法,它采用傅里叶条纹分析方法来求解干涉条纹的相位分布,运用基于BFGS的拟牛顿算法进行三坐标的迭代计算,确定点光源的三维空间坐标.通过计算机模拟实验,验证了该方法在有噪声干扰的情况下,可以有效降低干扰噪声对测量的影响.  相似文献   

8.
文章全面介绍了用条纹中心法自动分析处理等倾干涉条纹的详细过程 ,它通过对图像的二值化、细化、修补、标记等步骤来完成 ,涉及了条纹中心线的提取、条纹缺陷的消除、条纹直径的获取等关键内容。文章最后给出了试验的结果并讨论了等倾干涉条纹图的数字化自动分析处理软件的编制  相似文献   

9.
利用白光扫描干涉测量表面微观形貌   总被引:1,自引:0,他引:1  
由于激光显微干涉只能测量表面微观形貌的相对高度值,不能进行绝对测量,本文提出了白光显微干涉测量方法,研制了测量仪器,并对CCD像素格值和PZT进行了标定.该仪器以白光干涉理论为基础,利用空间频域算法计算白光干涉图零级条纹中心位置,根据零级条纹中心移动量来得到被测工件表面微观形貌.对被测工件表面进行了测试,试验结果表明:...  相似文献   

10.
二维多光束干涉图特征信息的提取   总被引:2,自引:1,他引:1  
较详细地阐述了一套新的运用于激光波长测量中对图像数据中特征信息的提取方法。由于是二维干涉图进行处理。所以处理过程基本上分为图像预处理阶段的图形增强和二值化、条纹图形细线化的预处理及细线化,以及图形标注和数据处理,与其它方法相比,采用这种处理方法,测量精度高,在测量速度方面也能满足实时处理的要求。  相似文献   

11.
倾斜校正是全自动生物芯片图像处理必不可少的环节。针对以往倾斜校正算法耗时过长,本文提出一种快速倾斜校正算法。算法首先用细胞神经网络(CNN)寻求各个样点的中心,然后进行Hough变换,再计算方差来寻求倾斜角,最后利用CNN灰度图像旋转模板进行倾斜校正。本算法利用了细胞神经网络并行处理的特性,并充分考虑了生物芯片图像的特点。理论分析与试验结果显示本文算法能够准确高速地完成倾斜校正。  相似文献   

12.
A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.  相似文献   

13.
In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.  相似文献   

14.
昆虫翅膀运动变形测量中弱光条图像处理   总被引:1,自引:0,他引:1  
陈大志  张广军 《光电工程》2005,32(5):85-88,96
通过在连续域内,建立光栅型结构光光条图像的数学模型,提出了一种用于提取昆虫翅膀运动变形测量中弱光条中心点的图像处理方法。该方法根据光条图像灰度的二阶方向导数极值条件,利用Hessian矩阵算法提取弱光条中心点的亚像素位置,通过连续性约束、方向约束和光条间距约束等特征约束条件去除图像处理过程中出现的噪声点。实验结果表明,该方法成功地将昆虫翅膀图像中的弱光条中心点与“虚假点”进行了分离。  相似文献   

15.
Measurement Techniques - A third-generation algorithm that implements the method of mirror noise images for extracting a useful signal from noise is developed. In the proposed algorithm initial...  相似文献   

16.
作为发射车的关键组成部件,滚动轴承的工作环境复杂,故障诊断困难。提出一种自适应深度卷积神经网络,针对传统CNN诊断方法存在的计算效率较低、参数调试需人工经验指导等问题,采用粒子群优化算法确定CNN模型结构和参数,应用主成分分析法将故障诊断特征学习过程可视化,评估其特征学习能力。将提出方法应用于发射车滚动轴承故障诊断,对比标准CNN、SVM、ANN诊断方法,10种工况的诊断结果表明,提出方法诊断精度高且鲁棒性好。  相似文献   

17.
k-中心点聚类算法(k-medoids cluster algorithm,KCA)是改进的机器学习聚类算法,该方法通过初始聚类中心选取和聚类中心更新,对无标记训练样本的学习揭示数据的内在性质及规律,从而区分出机器的运行状态。提出了一种正交小波变换k-中心点聚类算法(orthogonal wavelet transform k-medoids clustering algorithm,OWTKCA)诊断方法,利用正交小波变换(orthogonal wavelet transformation,OWT)方法提取各细节信号作为训练样本,用KCA方法进行分类。通过滚动轴承的试验数据分类结果显示,该方法相对于没有提取特征值的KCA能有效处理复杂机械振动信号,明显提高了故障数据聚类效果,缩短了聚类时间,提高了智能诊断效率。  相似文献   

18.
本文提出了一种基于支持向量机的坦克识别算法。在对图像预处理之后,运用颜色和纹理信息进行分割,采用基于数学形态学的算法求得边缘像素,提取具有RST不变性的轮廓特征向量,输入支持向量机进行训练和识别。将支持向量机与传统的人工神经网络的算法进行了对比实验,实验表明基于支持向量机的坦克识别算法具有更好的性能。  相似文献   

19.
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach, image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time-consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.  相似文献   

20.
Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.  相似文献   

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