An improved stair phase encoding method for absolute phase retrieval

An improved phase unwrapping method is proposed to reduce the projection fringes in three-dimensional (3D) surface measurement. Color fringe patterns are generated by encoding with sinusoidal fringe and stair phase fringe patterns in red and blue channels. These color fringe patterns are projected onto the tested objects and then captured by a color CCD camera. The recorded fringe patterns are separated into their RGB components. Two groups of four-step phase-shifting fringe patterns are obtained. One group of the stripes are four sinusoidal patterns, which are used to determine the wrapped phase. The other group of stripes are four sinusoidal patterns with the codeword embedded into stair phase, whose stair changes are perfectly aligned with the 2π discontinuities of sinusoidal fringe phase, which are used to determine the fringe order for the phase unwrapping. The experimental results are analyzed and compared with those of the method in Zheng and Da (2012. Opt Express 20(22):24139–24150). The results show that the proposed method needs only four fringe patterns while having less error. It can effectively reduce the number of projection fringes and improve the measuring speed.     

Iterative two-step temporal phase-unwrapping applied to high sensitivity three-dimensional profilometry

Although temporal phase unwrapping method can be applied to solve some problems to measure an object with steep shapes, isolated parts or fringe undersampling in three-dimensional (3D) shape measurement, it needs to acquire and process a sequence of fringe pattern images which would take much time. Servin et al. proposed a 2-step temporal phase unwrapping algorithm, which only needs the 2 extreme phase-maps to achieve exactly the same results as standard temporal unwrapping method. But this method is only validated by computer simulation, shortage of experimental demonstration, its sensitivity coefficient G is limited, and it cannot be used when the G value is larger. We proposed an iterative two-step temporal phase-unwrapping algorithm which is an extension of Servin׳s method. First, add a fringe pattern with an intermediate sensitivity, project the fringe patterns of different sensitivity onto the tested object’s surface, and collect deformed fringe patterns with a CCD camera. Then we obtain the unwrapped phase with larger sensitivity coefficient G by cascading the sensitivity coefficients. And we derive the initial phase conditions of the 2-step temporal phase unwrapping algorithm. Finally, the experimental evaluation is conducted to prove the validity of the proposed method. The results are analyzed and compared with Servin׳s method. The experimental results show that the proposed method can achieve higher sensitivity and more accurate measurement, and it can overcome the main disadvantages encountered by Servin׳s method.     

基于相似性分类的高光谱主成分融合方法比较

为了提高复杂场景弱小目标高光谱融合图像的质量,提出了基于相似性分类的主成分融合方法。光谱数据像素向量的相似性测度分类产生类矩阵,通过由类矩阵主成分变换的降维投影矩阵来投影变换原有光谱数据,获得降维数据矩阵。对比了传统PCA与基于欧式距离分类的PCA(ED_PCA)、基于光谱角分类的PCA(SA_PCA)、基于光谱信息散度分类的PCA(SID_PCA)和基于正交投影散度分类的PCA(OPD_PCA)四种改进方法的融合性能。实验结果表明:SA_PCA和SID_PCA方法兼具了ED_PCA和OPD_PCA的优点,对比度提升较好,阈值参数不敏感,运行时间较短。     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network

Multi-focus image fusion combines multiple source images with different focus points into one image, so that the resulting image appears all in-focus. In order to improve the accuracy of focused region detection and fusion quality, a novel multi-focus image fusion scheme based on robust principal component analysis (RPCA) and pulse-coupled neural network (PCNN) is proposed. In this method, registered source images are decomposed into principal component matrices and sparse matrices with RPCA decomposition. The local sparse features computed from the sparse matrix construct a composite feature space to represent the important information from the source images, which become inputs to PCNN to motivate the PCNN neurons. The focused regions of the source images are detected by the firing maps of PCNN and are integrated to construct the final, fused image. Experimental results demonstrate that the superiority of the proposed scheme over existing methods and highlight the expediency and suitability of the proposed method.     

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy(91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.     

Principal component analysis of non-uniformity in electrical characteristics of HgCdTe photodiode arrays

We present a method of analyzing the non-uniformity in electrical characteristics of HgCdTe photodiode arrays for infrared imaging applications. We have selected dynamic resistance–voltage (R–V) characteristics for analyzing electrical behavior of HgCdTe photodiodes because the dynamic resistance at a given operating voltage directly governs the imager performance and being derivative of current–voltage (I–V) characteristics, it has little impact of the constant shifts due to stray illumination during dark measurements, relaxing the stringent requirement of perfect dark conditions to some extent for performance analysis. We have demonstrated that by using statistical analysis such as correlation of the selected signatures and their principal component analysis, we can identify the root cause of the high non-uniformity among sensor pixels in the array. The method has been implemented using theoretical I–V model of MWIR HgCdTe photodiodes, but it is generic and may be implemented on any other types of diode arrays for theoretical or experimental analysis of their non-uniformity.     

基于主元分析的随机相移算法

将统计理论中的主元分析应用到干涉条纹分析中,提出一种基于主元分析的随机相移算法。该算法先将多帧条纹数据分解成不相关的主元成分,然后从主元成分中提取2维相位信息。数值模拟结果表明:该算法不需要迭代运算就能从4帧完全随机相移干涉图中有效提取相位信息,运算时间少,精度高。实验结果表明主元分析相移算法比现有迭代相移算法更适合于随机相移干涉图分析。     

Improvement of sensitivity using principal component analysis by dual focused ion beam time-of-flight secondary ion mass spectrometry

It is important to develop and to practically use the method to analyze a micro-nanometer order area. Especially, three-dimensional microanalysis for minute structure that consists of the organic compounds and the polymer is difficult. We developed a novel three-dimensional microanalysis method by means of focused ion beam (FIB) for section processing and ToF-SIMS for mapping method. For the purpose of realization of three-dimensional microanalysis and a chemical and structural analysis of the organic matter, the sensitivity improvement of ToF-SIMS in the three-dimensional analysis device and the method of the spectral analysis are examined. To improve the sensitivity of ToF-SIMS, the sample stage was modified to arrange perpendicularly with the ToF optical axis, and the distortion of electric field was corrected. And, by analyzing the fragment ions by using the principal component analysis (PCA) to raise the efficiency of the spectrum analysis, spatial resolution has improved. As a result, the resolution of the device improved to sub micrometer order, and advanced to the achievement of the three-dimensional microanalysis.     

改进的基于二维主分量分析的掌纹识别

主分量分析(PCA)是一种在众多生物特征识别中获得成功应用的特征提取技术,是一种基于二阶统计的在最小均方误差意义上的最优维数据压缩技术,它所提取的各特征分量之间是互不相关的。传统的PCA变换是对图像向量的分析,但向量维数一般都很高。二维主分量分析方法是最近兴起的针对图像矩阵的主分量分析方法,与一维主分量分析相比能更精确的计算原始数据的协方差矩阵。将其应用于掌纹识别,并在主分量的选取上加以改进,选取了更适合于分类的主分量。实验结果表明,该方法不仅有更高的识别率,而且维数更低。     

On the use of principal component analysis to speed up radiative transfer calculations

Radiative transfer is computationally expensive. However, it is essential to many applications, in particular remote sensing retrievals. Principal component analysis of the optical depth and single scattering albedo profiles has been proposed as a possible method to help ease the computational burden. Here we show how the technique could be applied to a practical problem of CO₂ retrievals from high spectral resolution measurements of reflected sunlight in three near infrared bands. We obtain a speed improvement of more than 50 fold (compared to monochromatic computations), while reproducing the radiances to better than 0.1% accuracy.     

Micro-phase measuring profilometry: Its sensitivity analysis and phase unwrapping

Global illumination affects shape measurement accuracy, and phase unwrapping is an important problem in phase measuring profilometry. In this paper, a micro-phase measuring profilometry is proposed to reduce the effects of global illumination. The wrapped phase error level of the proposed method is lower than that of the traditional micro-phase shifting method. First, a frequency selection rule is developed in combination with the micro-phase rule to design the frequencies of the proposed phase measuring profilometry. The frequency rule is obtained by analysing the uncertainty of the wrapped phase caused by intensity noise. Then, phase unwrapping is regarded as a congruence problem, and the closed-form robust Chinese remainder theorem algorithm is adopted to determine the correspondence. Finally, the comparative experiments are conducted on a projector-camera system. Experimental results show that the effects of global illumination can be effectively reduced with the proposed phase measuring profilometry, and the proposed frequency selection rule is valid. In addition, the performance of the robust Chinese remainder theorem in addressing the phase error is better than that of traditional Chinese remainder theorem. Furthermore, the unwrapping accuracy can nearly reach 100% if the frequency selection rule is satisfied. Otherwise, the performance degrades.     

基于亮温光谱和主成分分析的大气污染气体探测

在移动平台条件下,污染气体红外遥测系统无法获取有效的背景光谱。利用算法扣除红外光谱中的背景信息,提取目标光谱特征,是污染气体红外遥测系统在移动平台上应用的关键环节。提出基于亮温光谱和主成分分析的迭代拟合算法。相对于传统的亮温光谱法,该算法降低了亮温光谱法对仪器响应函数的精度要求,同时结合主成分分析和迭代拟合技术,可有效提取目标光谱特征。     

Principal component analysis algorithm in video compressed sensing

An algorithm of principal component analysis in video compressed sensing is proposed in the paper. Aiming at the compressed sensing problems of video sequences, the inter-frame correlation among the images is analyzed and the transform coefficients with lower value are removed according to the energy concentration characteristics of principal component analysis. Therefore, the sparse realization of video signals in the form of principal component analysis is accomplished and the possibility of the transformation being used in compressed sensing algorithm is verified. Finally, simulation results show that, with the comparison of the traditional algorithm based on wavelet transform, the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence, but also save the sampling resources. Moreover, it is more suitable for stream transmission of multimedia.     

基于位相测量轮廓术的三维数字化仪的实验研究

介绍了基于位相测量轮廓术的三维数字化仪的原理和系统结构 ,阐述了一种有效的三维数字化方法。该方法具有精度高、速度快、可实现自动测量等特点。     

Comparison of eight unwrapping algorithms applied to Fourier-transform profilometry

Phase unwrapping is a task common to many applications like interferometry imaging, medical magnetic resonance imaging, solid-state physics, etc. Fourier transform profilometry (FTP) values the height distribution of object, elaborating the interference between a plane reference grating and a deformed object grating. Since the height information is extracted from the phase of a complex function, the phase unwrapping is a critical step of the process. Several unwrapping algorithms are proposed in literature, but applied to measurement technologies different from FTP. The purpose of this paper is to define the performances of eight different unwrapping algorithms applied to FTP optical scan method and to define the best one. The algorithms chosen are: Goldstein's algorithm, quality guided path following method, Mask cut method, Flynn's method, multi-grid method, weighted multi-grid method, preconditioned conjugate gradient method and minimum L_p-norm method. The methods were tested on real images acquired by a FTP scanner developed and calibrated for these experiments. The objects used vary from simple geometries, like planes and cylinders, to complex shapes of common use objects. Algorithms were qualified considering the phase unwrapping errors, execution time and accuracy of the shape of objects obtained from the scan method in comparison with real ones. The results show that quality guided algorithm best fits in FTP application.     

Phase unwrapping algorithms, respectively, based on path-following and discrete cosine transform

This paper describes a path-following phase unwrapping algorithm and a phase unwrapping algorithm based on discrete cosine transform (DCT) which accelerates the computation and suppresses the propagation of noise. Through analysis of fringe pattern with serious noises simulated in mathematic model, we make a contrast between path-following algorithm and DCT algorithm. The advantages and disadvantages of analytical fringe pattern are also given through comparison of two algorithms. Three-dimensional experimental results have been given to prove the validity of these algorithms. Despite DCT phase unwrapping technique robustness and speed in some cases, it cannot be unwrapping inconsistencies phase. The path-following algorithm can be used in automation analysis of fringe patterns with little influence of noise.     

基于LGSPP-Bayes的故障检测与辨识方法

针对主元分析(Principal component analysis, PCA)和局部保持投影(Locality preserving projections, LPP)方法在降维过程中分别只能保留数据集的整体信息和局部信息,提出一种基于局部整体结构保持投影的贝叶斯故障检测与辨识方法(Local and global structure preserving projections and bayes, LGSPP-Bayes)。首先,将正常工况操作下的原始数据通过局部整体结构保持投影方法投影到低维特征空间,得到高维到低维的数据转换矩阵;然后通过设计贝叶斯分类器来进行故障检测;最后当检测到故障后通过计算贝叶斯分类函数的大小来识别故障种类。将LGSPP-Bayes方法应用于TE过程,仿真结果表明对故障的检测优于其他方法,并且可以很好地将故障种类识别出来。     

Phase measuring profilometry based on elliptically pattern grating

The present study proposes a new method for measuring the surface shape of an object using elliptically pattern grating projection. Based on phase measuring profilometry (PMP), elliptically pattern grating is projected on the surface of the object to measure its shape in 3D. The computing formula for solving the phase by the phase-shifting fringe method is derived, and the 3D shape formula for the altitude is reestablished. The error-contrastive analysis of the object's surface shape is re-established based on the proposed method and traditional PMP. The proposed method was shown to have a strong anti-noise ability and able to measure objects under high noise. More accurate measurement results were obtained by computer simulation and experiment to verify the feasibility of the proposed method.     

An improved quantum principal component analysis algorithm based on the quantum singular threshold method

Quantum principal component analysis (qPCA) is a dimensionality reduction algorithm for getting the eigenvectors corresponding to top several eigenvalues of the data matrix and then reconstructing. However, qPCA can only construct the quantum state contains all the eigenvectors and eigenvalues. In this paper, we present an improved quantum principal component analysis (Improved qPCA) algorithm with a fixed threshold. We can reduce the singular value less than the threshold to 0 and obtain a target quantum state which can be used to get an output similar to qPCA after phase estimation. Compared with qPCA, our algorithm has only the target eigenvalues and the probability that we get each target eigenvalue is greater. Furthermore, our algorithm can serve as an additional regularization method and a subroutine for subsequent data processing.