Application of iteration and finite element smoothing technique for displacement and strain measurement of digital speckle correlation

In this paper, an improved method for measuring displacement in digital speckle correlation technology, which is based on an iterative and spatial-gradient algorithm, is developed. After obtaining full-field displacement, both finite element method and 2D generalized cross-validation (GCV) algorithm are adopted for smoothing the displacement field, and then the strain field can be obtained from the smoothed displacement field. The method is estimated by simulated speckle patterns and three-point bending experiment. All the results show the improved method can obtain a reasonable estimation of displacement and strain fields in digital speckle correlation method.     

完全变光滑长度SPH法及其实现

提出完全变光滑长度SPH法及其算法实现.方程组基于对称形式核函数近似,SPH密度演化方程与变光滑长度方程隐式关联;在Springel提出的全守恒SPH方程组基础上,通过将分散核近似形式改进为对称核近似形式得到SPH动量方程和能量方程.采用迭代求解密度演化方程和变光滑长度方程,显式求解SPH动量方程和能量方程,增加的计算量相对很少.给出三个1D激波管算例和2D Sedov算例验证方法的有效性.数值结果表明,算法保持动量和能量的守恒律,解决了传统SPH法中由于变光滑长度影响带来的计算误差,且在模拟2DSedov问题时能得到比Springel方法更准确的压强峰值位置和中心压强值.特别适合于模拟爆炸与冲击、大变形大扭曲等密度梯度和光滑长度梯度剧烈变化的问题.     

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 2D strain estimator with numerical optimization method for soft-tissue elastography

Elastography is a bioelasticity-based imaging modality which has been proved to be a potential evaluation tool to detect the tissue abnormalities. Conventional method for elastography is to estimate the displacement based on cross-correlation technique firstly, then strain profile is calculated as the gradient of the displacement. The main problem of this method arises from the fact that the cross-correlation between pre- and post-compression signals will be decreased because of the signal’s compression-to-deformation. It may constrain the estimation of the displacement. Numerical optimization, as an efficient tool to estimate the non-rigid deformation in image registration, has its potential to achieve the elastogram. This paper incorporates the idea of image registration into elastography and proposes a radio frenquency (RF) signal registration strain estimator based on the minimization of a cost function using numerical optimization method with Powell algorithm (NOMPA). To evaluate the proposed scheme, the simulation data with a hard inclusion embedded in the homogeneous background is produced for analysis. NOMPA can obtain the displacement profiles and strain profiles simultaneously. When compared with the cross-correlation based method, NOMPA presents better signal-to-noise ratio (SNR, 32.6 ± 1.5 dB vs. 23.8 ± 1.1 dB) and contrast-to-noise ratio (CNR, 28.8 ± 1.8 dB vs. 21.7 ± 0.9 dB) in axial normal strain estimation. The in vitro experiment of porcine liver with ethanol-induced lesion is also studied. The statistic results of SNR and CNR indicate that strain profiles by NOMPA performs better anti-noise and target detectability than that by cross-correlation based method. Though NOMPA carry a heavier computational burden than cross-correlation based method, it may be an useful method to obtain 2D strains in elastography.     

A robust edge detection method with sub-pixel accuracy

In this paper, a new edge detection approach combining gray-moments operator with smoothing spline algorithm is proposed, which is invariant to additive and multiplicative noises in the image. This approach consists of two steps: firstly, a continuous blurred edge model is obtained using the smoothing spline algorithm in the edge region detected by Sobel operator; then a gray-moment solution is derived for both the one- and two-dimensional situations using the blurred edge model. Testing of this new detection approach demonstrates more robustness against the white Gaussian noise and speckle noise, and run time very close to the gray-moment and space-moment operators. The above advantages indicate this approach is very suitable for on-line accurate detection.     

Strain field denoising for digital image correlation using a regularized cost-function

In digital image correlation (DIC), the widely used forward-additive Newton–Raphson (FA-NR) algorithm and the recently introduced equivalent but more efficient inverse-compositional Gauss–Newton (IC-GN) algorithm are capable of providing both displacements and displacement gradients (strains) for each calculation point. However, the obtained displacement gradients are seriously corrupted by various noises, and for this reason these directly computed strains are usually considered as useless information and therefore discarded. To extract strain distributions more accurately, much research efforts have been dedicated to how to smooth and differentiate the noisy displacement fields using appropriate numerical approaches. In this contribution, contrary to these existing strain estimation approaches, a novel and alternative strain estimation approach, based on denoising the noisy strain fields obtained by FA-NR or IC-GN algorithm using a regularized cost-function, is proposed. The effectiveness and practicality of the proposed strain estimation technique is carefully examined using both computer-simulated images with imposed homogeneous and inhomogeneous deformation, and experimentally obtained images. Experimental results reveal that the strains obtained by the proposed method are comparable to those determined by post-processing of the displacement fields using conventional pointwise least squares strain estimation approach.     

Real-time application of critical dimension measurement of TFT-LCD pattern using a newly proposed 2D image-processing algorithm

A critical dimension measurement system for TFT-LCD patterns has been implemented in this study. To improve the measurement accuracy, an imaging auto-focus algorithm, fast pattern-matching algorithm, and precise edge detection algorithm with subpixel accuracy have been developed and implemented in the system.The optimum focusing position can be calculated using the image focus estimator. The two-step auto-focusing technique has been newly proposed for various LCD patterns, and various focus estimators have been compared to select a stable and accurate one.Fast pattern matching and subpixel edge detection have been developed for measurement. The new approach, called NEMC, is based on edge detection for the selection of influential points; in this approach, points having a strong edge magnitude are only used in the matching procedure. To accelerate pattern matching, point correlation and an image pyramid structure are combined.Edge detection is the most important technique in a vision inspection system. A two-stage edge detection algorithm has been introduced. In the first stage, a first order derivative operator such as the Sobel operator is used to place the edge points and to find the edge directions using a least-square estimation method with pixel accuracy. In the second stage, an eight-connected neighborhood of the estimated edge points is convolved with the LoG (Laplacian of Gaussian) operator, and the LoG-filtered image can be modeled as a continuous function using the facet model. The measurement results of the various patterns are finally presented.The developed system has been successfully used in the TFT-LCD manufacturing industry, and repeatability of less than 30 nm (3σ) can be obtained with a very fast inspection time.     

Vision-based vehicle detection and inter-vehicle distance estimation for driver alarm system

In this paper, we propose a robust real-time vehicle detection and inter-vehicle distance estimation algorithm for vision-based driving assistance system. The proposed vehicle detection method uses the combination of multiple vehicle features, which are the usual Harr-like intensity features of car-rear shadows and additional Haar-like edge features. The combination of two distinctive Haar-like intensity and edge features greatly reduces the false-positive vehicle detection errors in real-time. And, after analyzing two inter-vehicle distance estimation methods: the vehicle position-based and the vehicle width-based, we present a novel improved inter-vehicle distance estimation algorithm that uses the advantage of both methods. Various experimental results show the effectiveness of the proposed method.     

基于改进SIFT特征点匹配的图像拼接算法

基于传统SIFT方法和图像像素加权平滑融合的思想,提出了一种改进SIFT特征点的图像拼接方法。该方法首先利用Canny边缘检测算法获得图像的边缘点坐标,通过和SIFT算法关键点坐标进行对比,去除不稳定响应点;其次通过K-L变换降低算法复杂度,对得到的匹配点对,使用RANSAC算法进行提纯,计算投影变换模型参数;最后使用渐入渐出的加权融合算法平滑图像,消除图像之间的拼接缝隙。该算法的可行性和有效性通过实验结果可以得到证明。     

基于影像仪测量直线度误差的优化方法研究

针对影像仪测量直线度误差的特点,设计了一种改进的边缘检测模板进行边缘提取,并提出一种满足最小条件的直线度误差评定的方法区间距离算法来优化直线度误差的测量。通过采用影像法对光滑极限塞规的高精度测量实验,与传统边缘检测方法和斜率搜索法进行比较,实例结果表明,改进的边缘检测算法相对于传统检测模板计算卷积次数减少一半,可以提高测量速度,采用区间 距离算法与斜率搜索算法相比较,相同8组数据直线度误差相对误差不超过2%,平均计算速度提高0.01 s。实验验证在影像仪测量不同直径塞规直线度误差的自动化测量中,采用该优化方法可以节约计算时间4.45 s,并通过不同评定方式的比较,提出测量直线度误差最佳测量跨距在0.078 mm~0.104 mm的建议,对实际直线度误差测量具有指导意义。     

SLEE算法与Hough圆拟合在香烟小包拉线错牙检测中的研究

针对香烟生产中广泛存在的小包拉线错牙问题,提出一种基于图形识别的检测方法。利用图像校正、平滑滤波、迭代阈值分割、边缘提取对拉线图像进行预处理,再采用随机霍夫变换(RHT)对两个拉线U型切口进行圆拟合,进而根据两个圆心在垂直方向上的距离计算出拉线错牙偏移量。针对Sobel、Canny等获取的边缘存在较多冗余信息问题,提出了一种扫描线边缘提取(SLEE)算法。实验结果表明所提方法能有效地检测出烟包拉线的错牙程度,误差小于0.3mm且具有较好的鲁棒性。     

An enhanced non-uniformity correction algorithm for IRFPA based on neural network

Influenced by detector materials’ non-uniformity, growth and etching techniques, etc., every detector’s responsivity of infrared focal plane arrays (IRFPA) is different, which results in non-uniformity of IRFPA. And non-uniformity of IRFPA generates fixed pattern noises (FPN) that are superposed on infrared image. And it may degrade the infrared image quality, which greatly limits the application of IRFPA. Non-uniformity correction (NUC) is an important technique for IRFPA. The traditional non-uniformity correction algorithm based on neural network and its modified algorithms are analyzed in this paper. And a new improved non-uniformity correction algorithm based on neural network is proposed in this paper. In this algorithm, the desired image is estimated by using three successive images in an infrared sequence. And blurring effect caused by motion is avoided by applying implicit motion detection and edge detection. So the estimation image is closer to real image than the estimation image estimated by other algorithms, which results in fast convergence speed of correction parameters. A comparison is made to these algorithms in this paper. And experimental results show that the algorithm proposed in this paper can correct the non-uniformity of IRFPA effectively and it prevails over other algorithms based on neural network.     

基于数字图像处理技术的人脸检测算法研究

为了提高人脸检测的准确性及检测速度,需要对基于数字图像处理技术的人脸检测算法进行研究。使用当前方法进行人脸检测时,需要提取脸部特征数目较多、检测速度过慢,降低人脸检测效率。为此,提出一种基于数字图像处理技术的人脸检测算法。该方法首先获取人脸数字图像,通过拉开数字图像的灰度间距,使数字图像灰度均匀分布,进而提高数字图像对比度,使图像更加清晰,再通过Wiener维纳滤算法对处理后的数字图像进行图像平滑去噪,在此基础上使用Robert边缘检测算子方法对数字图像人脸边缘每个像素点检测,得到数字图像中人脸边缘的基本图像,将其输入到计算机数字图像处理系统中进行识别检测。实验仿真证明,所提算法在检测速度及准确性等方面具有明显的优势。     

An improved TOA estimation algorithm based on denoised MVDR for B5G positioning

This paper proposes an improved minimum variance distortionless response (MVDR) based TOA estimation algorithm for 5G NR signals under multipath environments. The proposed algorithm achieves high resolution by exploiting a large number of subcarriers of 5G signals and reduces the dimension of the covariance matrix involved in MVDR substantially by utilizing a novel smoothing scheme. Since MVDR requires a relatively high signal-to-noise ratio (SNR), a denoising method is used to improve the TOA estimation performance. Simulation results show that the proposed algorithm achieves much higher resolution than the Bartlett beamformer (BF) and the TOA estimation accuracy remains high over a wide range of SNRs.     

Improved multi-scale wavelet in pantograph slide edge detection

The mainstream methods of pantograph slide edge detection are based on canny operator and multi-scale wavelet. The former has good single edge response but the edge is fractured, the latter performs good edge continuity but contains excessive edge points. This paper combines the advantages of both methods and proposes as an improved multi-scale wavelet edge detection method based on canny criteria. Firstly we filtered the pantograph image with edge-preserving symmetric near neighbor filter. Secondly calculated the Gaussian wavelet modulus and arguments at all levels of scale, then suppressed the non-maxima value of modulus along the corresponding arguments. At last, we integrated the modulus drawings at all levels of scale, and connected edge with applicable dual-threshold. Experiments results show that the improved algorithm has both satisfactory performances in single edge response and edge continuity, it markedly improves the efficiency of edge detection algorithm. Peak signal to noise ratio (PSNR) analysis finds that the improved algorithm exceeds canny operator and traditional multi-scale wavelet edge detection. Moreover, it has higher positioning accuracy, clearer details and better noise performance.     

Target detection for low cost uncooled MWIR cameras based on empirical mode decomposition

In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.     

The combined effect of nonlinear filtration and window size on the accuracy of tissue displacement estimation using detected echo signals

In cardiac elastography, the regional strain and strain rate imaging is based on displacement estimation of tissue sections within the heart muscle carried out with various block-matching techniques (cross-correlation, sum of absolute differences, sum of squared differences, etc.). The accuracy of these techniques depends on a combination of ultrasonic imaging parameters such as ultrasonic frequency of interrogation, signal-to-noise ratio, size of a kernel used in a block-matching algorithm, type of data and speckle decorrelation. In this paper, we discuss the possibility to enhance the accuracy of the displacement estimation via nonlinear filtering of B-mode images before block-matching operation. The combined effect of a filter algorithm and a kernel size on the accuracy of the displacement estimation is analyzed using a 36-frame sequence of grayscale B-mode images of a human heart acquired by an ultrasound system operating at 1.77 MHz. It is shown that the nonlinear filtering of images enables to obtain the desired accuracy (less than one pixel) of the displacement estimation with smaller kernels than without filtering. These results are obtained for two filters--an adaptive anisotropic diffusion filter and a nonlinear Gaussian filter chain.     

A novel approach to segmentation and measurement of medical image using level set methods

The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain).Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis.     

An improved edge detection algorithm for depth map inpainting

Three-dimensional (3D) measurement technology has been widely used in many scientific and engineering areas. The emergence of Kinect sensor makes 3D measurement much easier. However the depth map captured by Kinect sensor has some invalid regions, especially at object boundaries. These missing regions should be filled firstly. This paper proposes a depth-assisted edge detection algorithm and improves existing depth map inpainting algorithm using extracted edges. In the proposed algorithm, both color image and raw depth data are used to extract initial edges. Then the edges are optimized and are utilized to assist depth map inpainting. Comparative experiments demonstrate that the proposed edge detection algorithm can extract object boundaries and inhibit non-boundary edges caused by textures on object surfaces. The proposed depth inpainting algorithm can predict missing depth values successfully and has better performance than existing algorithm around object boundaries.     

Improving motion estimation by accounting for local image distortion

Cardiac elastography is a useful diagnostic technique for detection of heart function abnormalities, based on analysis of echocardiograms. The analysis of the regional heart motion allows assessing the extent of myocardial ischemia and infarction. In this paper, a new two-stage algorithm for cardiac motion estimation is proposed, where the data is taken from a sequence of 2D echocardiograms. The method combines the advantages of block-matching and optical flow techniques. The first stage employs a standard block-matching algorithm (sum of absolute differences) to provide a displacement estimate with accuracy of up to one pixel. At the second stage, this estimate is corrected by estimating the parameters of a local image transform within a test window. The parameters of the image transform are estimated in the least-square sense. In order to account for typical heart motions, like contraction/expansion, translation and rotation, a local affine model is assumed within the test window. The accuracy of the new algorithm is evaluated using a sequence of 500 grayscale B-mode images, which are generated as distorted, but known copies of an original ROI, taken from a real echocardiogram. The accuracy of the motion estimation is expressed in terms of errors: maximum absolute error, root-mean-square error, average error and standard deviation. The errors of the proposed algorithm are compared with these of the known block-matching technique with cross-correlation and interpolation in the sub-pixel space. Statistical analysis of the errors shows that the proposed algorithm provides more accurate estimates of the heart motion than the cross-correlation technique with interpolation in the sub-pixel space.