Single fringe projection profilometry based on sinusoidal intensity normalization and subpixel fitting

A novel fringe projection profilometry using a single sinusoidal fringe pattern projected is proposed. Computer-generated sinusoidal fringe and uniform intensity patterns are firstly projected on a testing object by a liquid crystal display projector. The variable reflection intensity of a fringe pattern is then roughly normalized by division operation applied to the grabbed fringe and uniform intensity patterns projected. Fringe intensity is further normalized by employing an interpolation algorithm. The deformed sinusoidal pattern encoding object shape is converted to a wrapped phase map without using phase-shifting or Fourier transform. Computer simulation and experimental performance are evaluated to demonstrate the validity of the proposed method. The experimental results compared with those of the four-step phase-shifting and fast Fourier transform methods are also presented.     

Application of computer vision and laser interferometer to the inspection of line scale

In the paper machine vision, laser interferometer and coordinate measuring machine (CMM) are combined to develop a vision inspection system. The measurement capability of the developed system is investigated by measuring the distances between the lines on a standard line scale. The vision camera is used to replace the probe of the CMM to take the images of the interested lines on a line scale at two different positions. Meanwhile, the displacement of the CCD camera is measured using laser interferometer. Using subpixel edge localization and outlier-excluding least-squares regression, the distance between two interested lines is computed under an image plane coordinate system. By adding the displacement of the CCD camera measured using laser interferometer, the line space can be determined. Experiments have been performed repeatedly to measure the line space on the 1.00 and 300.0 mm line scales. Results indicate that the measured data only have a little fluctuation and are close to those obtained by the NML (National Measurement Laboratory, Taiwan).     

A Subpixel Image Restoration Algorithm

Abstract

In statistical image reconstruction, data are often recorded on a regular grid of squares, known as pixels, and the reconstructed image is defined on the same pixel grid. Thus, the reconstruction of a continuous planar image is piecewise constant on pixels, and boundaries in the image consist of horizontal and vertical edges lying between pixels. This approximation to the true boundary can result in a loss of information that may be quite noticeable for small objects, only a few pixels in size. Increasing the resolution of the sensor may not be a practical alternative. If some prior assumptions are made about the true image, however, reconstruction to a greater accuracy than that of the recording sensor's pixel grid is possible. We adopt a Bayesian approach, incorporating prior information about the true image in a stochastic model that attaches higher probability to images with shorter total edge length. In reconstructions, pixels may be of a single color or split between two colors. The model is illustrated using both real and simulated data.     

基于逆模型偏最小二乘法的高光谱亚像元目标探测方法研究

目前的高光谱亚像元目标探测算法主要是利用线性混合模型,在背景端元光谱已知的前提下,构造一定的检测算子逐像元判定是否含有亚像元目标。该算法需要较为精确的背景端元光谱先验信息,且目标探测结果受背景复杂度影响较大。从光谱维数据分析的角度出发,将逆模型偏最小二乘法(ILS)和马氏距离奇异值检测方法相结合进行亚像元小目标探测研究。首先建立目标光谱与像元光谱的逆模型,并对像元光谱与目标光谱进行正交变换预处理(SNV),再利用偏最小二乘算法(PLS)计算逆模型的回归系数向量,从而将多维的光谱信息降维,并突出目标光谱信息,最后计算各像元回归系数的马氏距离,判定马氏距离大于3σ的系数所对应的像元点为含有亚像元目标的像元点。该方法仅需要目标光谱的先验信息,对背景的复杂度不敏感。文章采用AVIRIS仿真数据将上述方法与常用的正交子空间投影算法(OSP)的计算结果进行比较,通过比较不同算法的受试者工作特征曲线(ROC)以及信噪比(SNR)变化曲线,证实了该算法具有较快的运算速度和较高的目标探测性能。     

Non-iterative spot center location algorithm based on Gaussian for fish-eye imaging laser warning system

The directional accuracy of threaten laser is highly dependent on the laser spot center location accuracy for the laser warning system. On the basis of analyzing the spot features on wide field of view (FOV) fish-eye imaging laser warning system, a refinement of existing methodology based on fitting a 2D Gaussian distribution for estimating the center of the spot is developed. The proposed procedure is using a linear least squares based algorithm to determine the best-fitting parameters of the Gaussian. However, in contrast to the standard Gaussian Fitting procedure, this new scheme gives rise to equations that are rigorously linear in the transformed unknown parameters which can be solved easily by linear least squares estimators. To validate the accuracy of the proposed algorithms, experimental approaches with a type of laser warning detector and simulated threaten laser in laboratory are used to quantify its performance. The experimental results show that this method outperforms significantly the commonly used location technique. It is less susceptible to the non-100% fill ratio of CMOS and large unsymmetrical optical aberration of fish-eye lens. The positioning precision in paraxial region and 80° incident angle region are 1/60 pixels, 1/30 pixels respectively. The biggest error is less than 1/20 pixels. The proposed method is suitable for wide FOV laser warning system for high accuracy and low computational complexity.     

Further improvement of edge location accuracy of double fiber spherical coupling sensor using orthogonal Jacobi–Fourier moments

In order to further improve the edge location accuracy of double fiber spherical coupling sensor, an approach based on OJFMs is proposed. In this paper, OJFMs with high order spatial moment and simple calculation is built. Then, its edge location method and fast discrete algorithm is proposed. Finally, the proposed method is tested by locating the edge of standard pattern and the image acquired by double fiber spherical coupling sensor with lot of noise. Experiment results show that the location accuracy is up to 0.05 pixel and 0.08 pixel for standard lines and standard circle, respectively. The accuracy of double fiber spherical coupling sensor is 0.026 pixel in 2σ band and a actual accuracy of 36 nm is obtained by OJFMs after a moving average filter (N = 7).     

A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences

One of the challenges in practical subpixel motion estimation is how to obtain high accuracy with sufficient robustness to both illumination variations and additive noise. Motivated by the fact that the normalized spatial cross-correlation is invariant to illumination, we introduce a gradient-based subpixel registration method by maximizing the digital correlation (DC) function between the reference and target frames. Such DC function is remodeled with the presence of image noise, yielding that the correlation coefficient is only sensitive to noise standard variance. To fairly suppress the noise corruption, not only the target frame but also the reference one is reformulated into Taylor gradient expression with half but opposite motion vector. The final solution to motion estimates can be approximated into a closed form by reserving first-order coefficient terms of unregistered motion variables. The error trend of approximated solution is discussed. Computer simulations and actual experiments’ results demonstrate the superiority of the proposed method to the LMSE-based method and ordinary DC method when illumination variations and noise exist. Among the experiments, the influences of real subpixel translation value and noise variance degree on accuracy are studied; correspondingly, an optimized iterative idea for big translations and the recommended noise level adaptive to our method are introduced.     

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.     

Application of an improved subpixel registration algorithm on digital speckle correlation measurement

Digital speckle correlation method (DSCM) has been widely used in experimental mechanics to obtain the surface deformation fields. One of the challenges in practical applications is how to obtain the high accuracy with far less computation complexity. To determine the subpixel registration of the DSCM, a high efficient gradient-based algorithm is developed in this paper. The principle is described and four different modes of the algorithm are given. Based on computer-simulated images, the optimal mode of the algorithm is verified through the comparison of computation time, optimal subset-region size and sensitivity of the four modes. The influences of speckle-granule size and speckle-granule density on accuracy are studied and a quantitative estimation of the optimal speckle-granule size range is obtained. As the applications of this method, the practical deformation measurements with the rigid body translation and rotation as well as an experiment on biomechanics are presented to certify the feasibility and the validity of the algorithm.     

The alignment problem of visual cryptography schemes

Pixel expansion is an important parameter for Visual Cryptography Schemes (VCS). However, most papers in the literature are dedicated to reduce pixel expansion on the pixel level, i.e. to reduce the number of subpixels that represent a pixel in the original secret image. It is quite insufficient since the final size of the transparencies of the VCS is affected not only by the number of the subpixels, but also by the size of the subpixels in the transparencies. However, reducing the size of the subpixels in the transparencies results in difficulties of alignment of the transparencies. In this paper, we consider the alignment problem in VCS, and prove that in order to visually recover the original secret image, it is not necessary to align the transparencies precisely. This study is restricted to the case when only one transparency is shifted.