Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements

Digital image correlation (DIC) method using iterative least squares algorithm (ILS) for displacement field measurement and pointwise least squares algorithm (PLS) for strain field measurement is proposed in this paper. A more general and practical intensity change model is employed with consideration of the linear intensity change of the deformed image, followed by an iterative least squares algorithm for calculating displacement field with sub-pixel accuracy. The concept of correlation function is not used in the ILS method, even though we prove that the algorithm is actually equivalent to the optimization of the sum of squared difference correlation function using improved Newton–Raphson method. Besides, different from the conventional strain estimation approaches based on smoothing the displacement fields first and followed by differentiation of the smoothed displacement fields, a simple yet effective PLS algorithm is proposed for extracting strain fields from the computed displacement fields. The effectiveness and accuracy of the proposed techniques is verified through numerical simulation experiments. A practical application of the algorithms to residual plastic deformation field measurement of GH4169 alloy subjected to tensile fatigue is also presented.     

A comparison of iterative algorithms and a mixed approach for in-line X-ray phase retrieval

Previous studies have shown that iterative in-line X-ray phase retrieval algorithms may have higher precision than direct retrieval algorithms. This communication compares three iterative phase retrieval algorithms in terms of accuracy and efficiency using computer simulations. We found the Fourier transformation based algorithm (FT) is of the fastest convergence, while the Poisson-solver based algorithm (PS) has higher precision. The traditional Gerchberg-Saxton algorithm (GS) is very slow and sometimes does not converge in our tests. Then a mixed FT-PS algorithm is presented to achieve both high efficiency and high accuracy. The mixed algorithm is tested using simulated images with different noise level and experimentally obtained images of a piece of chicken breast muscle.     

Path-independent digital image correlation with high accuracy,speed and robustness

The initial guess transferring mechanism is widely used in iterative DIC algorithms and leads to path-dependence. Using the known deformation at a processed point to estimate the initial guess at its neighboring points could save considerable computation time, and a cogitatively-selected processing path contributes to the improved robustness. In this work, our experimental study demonstrates that a path-independent DIC method is capable to achieve high accuracy, efficiency and robustness in full-field measurement of deformation, by combining an inverse compositional Gauss–Newton (IC-GN) algorithm for sub-pixel registration with a fast Fourier transform-based cross correlation (FFT-CC) algorithm to estimate the initial guess. In the proposed DIC method, the determination of initial guess accelerated by well developed software library can be a negligible burden of computation. The path-independence also endows the DIC method with the ability to handle the images containing large discontinuity of deformation without manual intervention. Furthermore, the possible performance of the proposed path-independent DIC method on parallel computing device is estimated, which shows the feasibility of the development of real-time DIC with high-accuracy.     

Assessment of measuring errors in DIC using deformation fields generated by plastic FEA

In this article, systematic errors that arise from different implementations of digital image correlation (DIC) techniques are analyzed. In particular, we investigate the influence of the adopted correlation function, the interpolation order, the shape function and the subset size on the derived displacements. These errors are estimated using numerically deformed images that were obtained by imposing finite element (FE) displacement fields on an undeformed image yielding plastic deformation of the specimen. This FE procedure simulates realistic experimental heterogeneous deformations at various load steps. It is shown that DIC is able to reproduce these displacements up to a satisfactory level if conscious choices in the above-mentioned implementations are made.     

Digital image correlation based on a fast convolution strategy

In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss–Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.     

基于非线性光谱混合模型的遥感影像提取城市不透水层

针对传统方法在提取城市不透水层中的许多局限性,采用两种非线性光谱混合分解模型,包括混合调谐匹配滤波和多层感知器神经网络,通过混合像元分解获取城市不透水层.混合调谐匹配滤波利用用户选择的端元,通过最大化端元响应并减少未知背景信息的影响,进行局部分解端元.多层感知器由多个感知器组成,能够很好的进行非线性学习.对Landsa...     

特征提取算法在福美双表面增强拉曼光谱定量分析中的应用

表面增强拉曼散射(SERS)技术具有快速、指纹效应与极低的检测限等优点,被越来越多地应用到有害污染物、有毒物质、危险物质的检测与分析中。在SERS光谱的测量过程中,易受基底、仪器、宇宙射线与测量环境等因素影响,出现波动现象,对后续的分析与检测造成较大的干扰。基于农药福美双SERS光谱数据,尝试利用多种特征提取算法,如主成分分析(PCA)、离散余弦变换(DCT)、非负因式分解(NMF) ,对光谱的主分量进行提取,以减弱光谱数据波动对其后续的定量分析结果的影响。然后将提取后的分量分别结合线性回归算法——偏最小二乘法回归(PLSR),非线性回归算法——支持向量机回归(SVR)建立定量模型。最后,利用5-折交叉验证方法对比不同特征提取算法在不同类型的回归算法下的效果。通过实验验证可知,SVR对福美双溶液的分析精度要明显高于PLSR,这主要是由于SERS光谱强度与被分析物浓度之间为非线性关系。同时针对两种类型回归算法,特征提取算法都能明显地提升了分析结果,主要是由于其提取了源数据的主体信息,去除干扰信息。其中在线性回归中使用PCA效果最佳,在非线性拟合中使用NMF结果最佳,分析误差最好时可降低近3倍。最优回归模型(NMF+SVR)的交叉验证均方误差(RMSECV)为0.0455 μmol·L-1(10-6 mol·L-1),达到国家对福美双的检测标准,为农药快速检测提供一种新的方法。     

双点干涉法位相缺陷检测中的解相算法比较

双点光源移相干涉测量是大口径光学元件位相缺陷检测的一种重要方法。为了分析双点干涉中误差对解相算法的影响,首先给出相位缺陷检测的系统结构和理论模型,在此基础上,针对测量过程中主要存在的一次移相误差、二次移相误差、光强误差和随机振动误差,研究了Hariharan 5帧移相算法、13帧移相算法和迭代随机移相算法的解相误差,并进行了仿真分析。结果表明,针对这几种误差源,13帧算法解相精度整体优于5帧法,迭代随机移相算法解相效果优于13帧法和5帧法,当这几种误差按实际指标同时作用时,迭代随机移相算法解相误差RMS小于5帧法和13帧法,PV值稳定在0.5 nm以内。由于随机振动占主要作用,说明迭代随机移相算法受误差影响很小。     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

Iterative algorithm for spatial and intensity normalization of MEMRI images. Application to tract-tracing of rat olfactory pathways

Manganese (Mn)-enhanced magnetic resonance imaging (MEMRI) is an emerging technique for visualizing neuronal pathways and mapping brain activity modulation in animal models. Spatial and intensity normalizations of MEMRI images acquired from different subjects are crucial steps as they can influence the results of groupwise analysis. However, no commonly accepted procedure has yet emerged. Here, a normalization method is proposed that performs both spatial and intensity normalizations in a single iterative process without the arbitrary choice of a reference image. Spatial and intensity normalizations benefit from this iterative process. On one hand, spatial normalization increases the accuracy of region of interest (ROI) positioning for intensity normalization. On the other hand, improving the intensity normalization of the different MEMRI images leads to a better-averaged target on which the images are spatially registered. After automatic fast brain segmentation and optimization of the normalization process, this algorithm revealed the presence of Mn up to the posterior entorhinal cortex in a tract-tracing experiment on rat olfactory pathways. Quantitative comparison of registration algorithms showed that a rigid model with anisotropic scaling is the best deformation model for intersubject registration of three-dimensional MEMRI images. Furthermore, intensity normalization errors may occur if the ROI chosen for intensity normalization intersects regions where Mn concentration differs between experimental groups. Our study suggests that cross-comparing Mn-injected animals against a Mn-free group may provide a control to avoid bias introduced by intensity normalization quality. It is essential to optimize spatial and intensity normalization as the detectability of local between-group variations in Mn concentration is directly tied to normalization quality.     

激光散斑的亚像素位移法计算及比较

根据激光散斑相关测量中采用的插值法、梯度算法和牛顿一拉夫森迭代法3种亚像素位移算法的特点,逐一在亚像素尺度计算出磁流体干燥过程中的同一激光散斑图的偏均值和标准差变化曲线。基于得到的偏差均值和标准差讨论了3种算法处理激光散斑图的特点,比较了3种算法处理散斑图的计算精度、稳定性和计算用时。结果显示,在小变形的亚像素位移测量时,梯度算法的精度较高,且较为稳定,计算效率也较牛顿一拉夫森算法快约9倍。因此,在进行小变形亚像素位移测量时,采用梯度算法计算激光散斑是有效的。     

几种自适应IIR陷波器的复数算法及其应用

在自适应IIR陷波器实数算法的基础上,本文提出了三种基于梯度的自适应IIR陷波器的复数算法,并进行了仿真比较。结果表明,这三种复数算法均可检测复信号,并估计信号频率,其中"改进的"简化格型IIR陷波器复数算法收敛速度快、低信噪比下稳定性能好。湖上试验表明该算法不仅实时性好,而且在低信噪比下的检测效果令人满意。     

梯度算子选择对基于梯度的亚像素位移算法的影响

基于梯度的亚像素位移算法中不同的灰度梯度计算方法对计算结果的影响进行了研究,列出了几种常用灰度梯度计算方法,并用对相邻5像素点的离散灰度进行三次样条插值的方法推导出两种灰度梯度算子。然后对计算机模拟生成的散斑图像对用各种梯度算子计算得出的亚像素位移与预先设定的真实值做了比较,给出了相应的均值误差和均值相对误差。最后的真实试件的刚体平移和单向拉伸实验结果表明,Barron算子是所列的几种梯度算法中最为精确、稳定的梯度算子。     

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

In order to characterize errors of Digital Image Correlation (DIC) algorithms, sets of virtual images are often generated from a reference image by in-plane sub-pixel translations. This leads to the determination of the well-known S-shaped bias error curves and their corresponding random error curves. As images are usually shifted by using interpolation schemes similar to those used in DIC algorithms, the question of the possible bias in the quantification of measurement uncertainties of DIC softwares is raised and constitutes the main problematic of this paper. In this collaborative work, synthetic numerically shifted images are built from two methods: one based on interpolations of the reference image and the other based on the transformation of an analytic texture function. Images are analyzed using an in-house subset-based DIC software and results are compared and discussed. The effect of image noise is also highlighted. The main result is that the a priori choices to numerically shift the reference image modify DIC results and may lead to wrong conclusions in terms of DIC error assessment.     

Strain measurement of SU-8 photoresist by a digital image correlation method with a hybrid genetic algorithm

Digital image correlation (DIC) is a whole-field and non-contact strain measuring method. It could provide deformation information of a specimen by processing two digital images that are captured before and after the deformation. To search the deformed images, a hybrid genetic algorithm, in which a simulated annealing mutation process and adaptive mechanisms are added to the real-parameter genetic algorithm, is proposed in this work. To increase the accuracy and reliability of this method, some key parameters of this method are suggested. Then, this method is used to measure the strain during the micro tensile testing of SU-8 photoresist. In addition to the conventional single region, a double region is proposed to calculate the strain by DIC. The results indicate that while the strains obtained by single region are reasonable, those obtained by double region are accurate. Also the mechanical properties of SU-8 could be accurately obtained.     

Digital image correlation with gray gradient constraints: Application to spatially variant speckle images

As a carrier of local deformation information, speckle pattern inside a subset is usually crucial for surface displacement acquisition based upon a digital image correlation (DIC) method, since both accuracy and precision of DIC method are closely related to the amount of speckle information in a subset. Although some comprehensive theoretical frameworks have been developed to estimate the quality of local speckle patterns, it is still a great challenge how to effectively integrate the subset speckle information into the well-developed correlation criteria used for DIC. By means of a well-designed square window function, we here propose the concept of continuous subset in order to modulate subset size in a continuously derivable manner. Afterwards, we further develop a new constrained zero-normalized sum-of-squared differences (CZNSSD) criterion and construct the corresponding iterative algorithm, based on which the subset size involved can be automatically determined according to the necessary amount of speckle information. Numerical results of synthetic speckle images indicate that the set of algorithm can enhance the accuracy and precision of displacement measurement, especially for spatially variant speckle images.     

A novel speckle pattern—Adaptive digital image correlation approach with robust strain calculation

Digital image correlation (DIC) has seen widespread acceptance and usage as a non-contact method for the determination of full-field displacements and strains in experimental mechanics. The advances of imaging hardware in the last decades led to high resolution and speed cameras being more affordable than in the past making large amounts of data image available for typical DIC experimental scenarios. The work presented in this paper is aimed at maximizing both the accuracy and speed of DIC methods when employed with such images. A low-level framework for speckle image partitioning which replaces regularly shaped blocks with image-adaptive cells in the displacement calculation is introduced. The Newton-Raphson DIC method is modified to use the image pixels of the cells and to perform adaptive regularization to increase the spatial consistency of the displacements. Furthermore, a novel robust framework for strain calculation based also on the Newton-Raphson algorithm is introduced. The proposed methods are evaluated in five experimental scenarios, out of which four use numerically deformed images and one uses real experimental data. Results indicate that, as the desired strain density increases, significant computational gains can be obtained while maintaining or improving accuracy and rigid-body rotation sensitivity.     

Noise robustness and parallel computation of the inverse compositional Gauss–Newton algorithm in digital image correlation

The inverse compositional Gauss–Newton (IC-GN) algorithm is one of the most popular sub-pixel registration algorithms in digital image correlation (DIC). The IC-GN algorithm, compared with the traditional forward additive Newton–Raphson (FA-NR) algorithm, can achieve the same accuracy in less time. However, there are no clear results regarding the noise robustness of IC-GN algorithm and the computational efficiency is still in need of further improvements. In this paper, a theoretical model of the IC-GN algorithm was derived based on the sum of squared differences correlation criterion and linear interpolation. The model indicates that the IC-GN algorithm has better noise robustness than the FA-NR algorithm, and shows no noise-induced bias if the gray gradient operator is chosen properly. Both numerical simulations and experiments show good agreements with the theoretical predictions. Furthermore, a seed point-based parallel method is proposed to improve the calculation speed. Compared with the recently proposed path-independent method, our model is feasible and practical, and it can maximize the computing speed using an improved initial guess. Moreover, we compared the computational efficiency of our method with that of the reliability-guided method using a four-point bending experiment, and the results show that the computational efficiency is greatly improved. This proposed parallel IC-GN algorithm has good noise robustness and is expected to be a practical option for real-time DIC.     

近红外光谱结合化学计量学的常见中国蜂蜜掺杂糖浆鉴别

目前我国蜂蜜市场掺假现象严重,研究一种快速、准确的方法用于市场流通领域掺假蜂蜜的鉴别具有重要的现实意义。采用近红外光谱（NIR）结合化学计量学方法对常见的天然蜂蜜以及掺假（掺杂常见糖浆）蜂蜜进行建模识别,并比较偏最小二乘-判别分析（PLS-DA）及支持向量机（SVM）对糖浆掺假蜂蜜鉴别模型的影响。首先,采集来自中国10个省份、20种常见蜂蜜的112个天然纯蜂蜜样品,以及6种常见糖浆样品按不同糖浆含量（10%,20%,30%,40%,50%,60%）配制的112个掺假蜂蜜样品,共计224个样品;通过近红外光仪器扫描获得所有样品的近红外光谱数据（波长范围400~2 500 nm）;然后,分别采用一阶导数（FD)、二阶导数（SD）、多元散射校正（MSC)、标准正态变化(SNVT)四种方式对原始光谱进行预处理;再结合PLS-DA和SVM建立天然蜂蜜和糖浆掺假蜂蜜的鉴别模型,比较不同预处理方法对两种不同建模算法建立的蜂蜜掺假鉴别模型效果。其中SVM算法的惩罚参数c和核函数参数g通过网格搜索法（GS）、遗传算法（GA）、粒子群算法（PSO）三种寻优算法进行优化。分析结果表明：光谱数据进行预处理后所建立的模型准确率均有明显提升,而对于SVM模型,惩罚参数c和核函数参数g对模型准确率的提升效果要比光谱预处理带来的提升效果更明显。在PLS-DA算法中,经FD光谱预处理后建立的模型效果最好,最佳PLS-DA模型准确率为87.50%;在SVM算法中,经MSC预处理后,再通过GS寻优,获得惩罚参数c为3.0314,核函数参数g为0.3298的条件下所建立的模型效果最好,最佳SVM模型准确率为94.64%。由此可见,非线性的SVM算法结合NIR光谱数据所建立的天然蜂蜜与糖浆掺假蜂蜜鉴别模型要优于线性的PLS-DA模型,同时表明NIR光谱结合化学计量学方法对常见糖浆掺杂的中国蜂蜜鉴别是可行的。     

改进的QGA-ELM算法水稻叶面积指数反演模型

为了通过植被指数（VI）准确、可靠的获取不同施肥梯度、不同品种的水稻叶面积指数（LAI）,提出了一种基于改进的QGA-ELM算法应用于水稻LAI反演。首先通过8折交叉验证确定极限学习机(ELM)最佳的隐含层神经元个数与隐含层激活函数类型,再通过引入组合动态旋转角策略、单点混沌交叉操作、混沌变异操作、确定性选择策略、量子灾变操作对量子遗传算法（QGA）进行改进,最后使用改进后的QGA算法优化ELM神经网络输入层到隐含层的连接权值和隐含层的阈值。为了验证该模型普适性和有效性,依次建立多元线性回归、BP、ELM、QGA-ELM、改进的QGA-ELM算法5种模型,并在不同数据集上进行反演效果比较,结果表明：（1）对比QGA-ELM算法和改进的QGA-ELM算法进化过程,改进的算法能有效提升模型寻优能力,避免算法早熟,且能寻得更优结果。（2）对比五种算法在不同数据集上的反演效果,验证了NDVI,RVI与LAI之间主要为非线性关系,且ELM神经网络模型反演效果要优于BP神经网络模型和多元线性回归模型。（3）对比五种算法在不同数据集上的反演效果,改进的QGA-ELM算法绝大部分情况下拥有最高的反演精度和最低的误差,改进后的算法反演精度得到了明显提升,泛化性能也得到了增强。（4）改进的QGA-ELM算法在各种施肥梯度上均具有最高反演精度和最低误差,且精度较高,能为不同生长状况水稻LAI反演提供依据。（5）五种模型对庆和香LAI反演精度均要高于龙稻18,而改进的QGA-ELM算法在不同水稻品种上依然具有较高的反演精度,且在不同水稻品种上反演精度相差极小,远低于其他四种模型,能很好适应不同水稻品种LAI反演要求,极大提升模型的稳定性性,为不同水稻品种反演提供参考意义。