Gray Gradient Research Articles

Synchronous monitoring of axial vibration and rotation speed of rotating cylinder by linear array scanning

The axial vibration (AV) and rotation speed (RS) signals of a rotating cylinder contain rich condition information. In order to harness such quantitative information, an effective measurement method for synchronous monitoring of the AV and RS is highly desirable. However, traditional approaches are restricted either by limited test distance or multiple isolated sensors, which makes the approaches impractical to synchronously acquire the AV and RS signals through a single device. In this paper, a generalized visual measurement method for synchronous monitoring of the AV and RS via linear array scanning (LAS) is proposed. The basic idea is to paste a mark containing a printed black continuous bar and a black block (BCBB) on the surface of a rotating cylinder, and use a linear array camera to synchronously sample the bar and blocks. For the extraction of mark edges, we propose a single edge pixel extraction method through gray gradient of row pixels based on the observation of relatively stable gray level distribution. Line scanned signals disturbed by seam error or axial offset are automatically corrected through seam compensation and axial offset correction respectively. Experimental validation of the proposed method is presented for the cylinder under static hammering, variable speed condition and steady speed condition at a scanning distance of 500 mm. Bilinear interpolation is applied to the scanned linear array images before extracting the edge features and helps improve measurement accuracy. Analyses in the time domain and frequency domain verify the effectiveness of the LAS. The average error between the RS signal obtained by LAS and the reference value is 0.1420 rpm. The correlation coefficient between the AV time-domain signal of LAS and the reference signal is up to 0.900 and the resonance frequency error in the frequency spectrum is less than 1 Hz.

基于<bold>CT</bold>成像和数字体图像相关法的岩石内部变形场量测方法的研究进展

<p indent="0mm">Rock mechanics plays a critical role in mining engineering, slope engineering, tunnel engineering, water conservancy, hydropower engineering, and some newly developing rock mass engineering, such as deeply buried oil and gas storage, underground nuclear waste repository, geothermal development, etc. The evolution of the internal discontinuous structure of rock under the coupling of multiple physical fields such as stress field, seepage field, and temperature field is an important research topic in the field of rock mechanics. Scholars have investigated the internal structure characteristics and their evolution process at different scales with various equipment. High-resolution microtomography (Micro CT) with micron or even nanoscale resolution has been widely used in internal structure detection, digital core modeling, and numerical modeling of rock. The digital volume correlation (DVC) method can quantitatively analyze and visualize the internal 3D deformation field of rock, and its combination with Micro CT provides a new method for transparent analysis and deduction of discontinuous structures and multi-physical fields in rocks. This paper systematically reviews the research on internal deformation measurement of rocks by using Micro CT and DVC in terms of <italic>in-situ</italic> loading device with Micro CT, the theory of DVC, the influence of micro/meso-structures in rocks, and experimental measurements. To view the internal structures of rocks under different physical fields, various <italic>in-situ</italic> loading devices combined with Micro CT were developed, e.g., uniaxial loading device, triaxial loading coupled with seepage device, gas adsorption device, and heating device. In these devices, factors such as the strength of rock, physical field, material strength of chamber, X-ray energy and penetration, real-time performance of image acquisition, and quality of reconstructed images should be comprehensively considered. By using <italic>in-situ</italic> loading devices and Micro CT, a series of volume images of rocks are obtained before and after deformation, and then DVC is adopted to calculate the three-dimensional deformation field by analyzing the correlation between these volume images. According to the registration algorithms, DVC can be categorized into local DVC (L-DVC) and global DVC (G-DVC). The basic principle and development of DVC are reviewed. Due to the limitation of the heterogeneity of rocks and the accuracy of the image acquisition equipment, natural micro structures in rocks do not have ideal speckle patterns, which affects the accuracy of DVC. It is indicated that the larger the average gray gradient and Shannon entropy and the smaller the structural variation coefficient of a rock image, the higher the accuracy of DVC. The accuracy of G-DVC is slightly better than that of L-DVC, but its calculation efficiency is lower, especially when the mesh size is small. The applications of DVC for investigating strain localization in rocks, measuring carbon dioxide-induced strain in coal, and oil shale pyrolysis are presented. Despite some impressive achievements, DVC is still faced with some core challenges, e.g., the low accuracy of the DVC algorithm and artifact error correction, the influence of the microstructures in rocks, parameter identification and numerical model verification of rocks, and deformation field measurement under the coupling of multiple physical fields. With the popularization of Micro-CT in laboratories, DVC will be further applied to measuring the three-dimensional deformation field in rocks.

Chessboard Corner Detection Based on EDLines Algorithm.

To improve the robustness and accuracy of the corner-detection algorithm, this paper proposes a camera-calibration method based on the EDLines algorithm for the automatic detection of chessboard corners. The EDLines algorithm is initially used to perform straight-line detection on the calibration image. The features of the broken straight lines at the corners are then used to filter the straight lines and remove the background straight lines outside the chessboard. The pixels in the rectangular area around the filtered straight line are sorted by the gray gradient. After using the sorted results to fit the straight line, the coordinates of the intersection of the straight lines are taken as the initial coordinates of the corners and perform subpixel optimization on them. Finally, the corner points are sorted by the conversion between pixel-coordinate systems. The camera exposure time changes and complex imaging-background experiments show that the algorithm has no missed detection and redundancy in corner detection. The average reprojection error is found to be less than 0.05 pixels, which can be used in actual calibration.

Continuous space ant colony algorithm for automatic selection of orthophoto mosaic seamline network

This paper proposes a novel method for selecting orthophoto mosaic seamline network using a continuous space ant colony algorithm. The method first establishes a cost function based on gray differences and a gray gradient in the overlapping area. Then, a continuous space ant colony algorithm is established to iteratively select a least cost path for the optimal mosaic seamline. This proposed algorithm is carried out in three steps: (1) generate the initial mosaic network using area Voronoi diagrams with overlap; (2) adjust the vertices (The intersection of mosaic seamline network); and (3) search the seamlines using the continuous space ant colony algorithm to obtain the globally optimal mosaic seamline network. Two groups of experiments were performed using different numbers of orthophotos. The results demonstrate that our method saves 50 s and 53 s compared with the commercial software OrthoVista in Dataset 1 and Dataset 2, and saves 19 s and 22 s compared with Dijkstra in Dataset 1 and Dataset 2, respectively. The average cost of the mosaic seamline network generated by our method is the lowest (15.56) out of OrthoVista (21.79), Dijkstra (18.35) and Qmosaic (18.68) in Dataset 1, and the lowest (19.28) out of OrthoVista (26.33), Dijkstra (22.60) and Qmosaic (23.56) in Dataset 2.

A polarization-based image restoration method for both haze and underwater scattering environment

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

Direct Visual Odometry by Fusing Luminosity and Depth Information

In the traditional direct visual odometry, it is difficult to satisfy the photometric invariant assumption due to the influence of illumination changes in the real environment, which will lead to errors and drift. This paper proposes an improved direct visual odometry system, which combines luminosity and depth information. The algorithm proposed in this paper uses Kinect 2 to collect RGB images with the corresponding depth information, and selects points with large changes of gray gradient to construct a luminosity error function and uses the corresponding depth information to construct a depth error function. The two error functions are merged into one function and converted into the least squares function of the pose of camera, the Levenberg-Marquardt algorithm is used to solve the camera pose. Finally, the Graph optimization theory and the g2o library are used to optimize the initial pose. Experiments show that the algorithm can reduce the error to a certain extent and reduce the drift caused by illumination changes.

Hyper-Temporal Data Based Modulation Transfer Functions Compensation for Geostationary Remote Sensing Satellites

Over the past years, the acquisition of hyper-temporal data (HTD) from geostationary orbit remote sensing satellites (GEORSS) has provided numerous new research opportunities. Many factors influence the in-orbit dynamic modulation transfer function (MTF) of GEORSS, making it difficult to satisfy the requirements for space-borne cameras. The MTF compensation (MTFC) technique can effectively optimize the design of dynamic MTFs for GEORSS. The traditional MTFC methods mainly consider the sensor, atmosphere and relative motion of the satellite platform when improving GEORSS image quality. They will introduce new high frequency noise, resulting in image information loss. In this paper, a mixed sparse higher-order non-convex total variation (MS-HONCTV) model-aided MTFC method is proposed. By introducing the group sparse regularization (GSR) term into the MS-HONCTV model, it increases the robustness to noise and hence reduces the degeneration of the MTF produced by satellite’ low pointing stability. The MS-HONCTV model is then applied to solve the problem of image degradation. The quality of remote sensing data is improved by the proposed MTFC and this is achieved without modifying the aperture diameter, focal length, or detector size of the satellite’s optical system. Experimental results show that the proposed MS-HONCTV effectively improves the images’ MTF, SNR, gray mean gradient (GMG) and standard deviation (SD), as evidenced by subjective qualitative analysis and objective quantitative assessments of simulated data, laboratory data, and GF-4 satellite data. Compared with other methods, the SNR of the proposed method is increased by 30%, GMG by 14.21% and SD by 6.3% on average.

An Auto-Focusing Evaluation Function Adapted to Multi-Directional Gray Gradient Change

An Auto-Focusing Evaluation Function Adapted to Multi-Directional Gray Gradient Change

Local Kikuchi band detection in electron backscatter diffraction patterns for enhanced pattern indexing.

Partially missing bands in the preset region for Hough transform might strongly affect the accuracy of indexing Kikuchi patterns and reduce the quality of electron backscatter diffraction (EBSD) maps. This paper proposes a novel local band detection method for such kind of low-quality patterns. The approach involves rotating bands to vertical direction, detecting the local line segments through calculating the largest horizontal average grey gradient with a constant interval of 400 pixels in vertical direction, and applying Hough transform as well as weighted averaging to these line clusters to unify the edges of Kikuchi band. Therefore, even if only part of a Kikuchi band is visible, the entire band can also be accurately extracted. The average interplanar angle error obtained by the proposed method is approximately 29.0% less than those obtained by Hough transform-based technique. Moreover, the comparison of mean angular deviation (MAD) is also discussed. The average MAD of this method is about 38.5% lower than that of Hough transform-based technique. Consequently, the local Kikuchi band detection method is expected to be used for post-processing and re-indexing the EBSD low-quality patterns.

An iterative reconstruction method for sparse-projection data for low-dose CT.

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

Method for acquiring accurate coordinates of the source point in electron backscatter diffraction

An electron backscatter diffraction device is an important accessory for a scanning electron microscope and can provide crystal structure orientation and phase content data through analysis of electron backscatter diffraction patterns. The acquisition of these data depends on pattern indexing, including interplanar angle calculation and crystal plane indexation. The coordinates of the source point are key points for interplanar angle calculation, and they vary with the movement of the incident beam. In this study, we first combined the grey gradient calculation with screen moving method to achieve accurate positioning of source point and obtained coordinates of source point with sub-pixel precision. The errors of three coordinates were 0.07%, 0.06% and 0.04%, respectively. By using this coordinate of source point to conduct interplanar angle calculation the maximum error was 0.53°, which was a good proof of the accuracy of source point positioning. Then we established the relationship between source point coordinates variation and incident beam movement. Coordinates can be given out based on the displacement of beam directly. And to illustrate the accuracy, interplanar angle calculation was performed and the maximum error was 0.81°. This means that the relationship between variation of source point coordinates and beam movement is highly accurate.

A Fast Otsu Thresholding Method Based on an Improved 2D Histogram

The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the optimal threshold, thus reducing the amount of computation. The simulation results demonstrate that the proposed algorithm has better performance in image segmentation, with the increased computational speed and improved real-time capability.

Online nondestructive evaluation of TBC crack using infrared thermography

Thermal barrier coatings (TBCs) are widely used in gas turbines. Crack is one of the most serious failure modes in TBCs. In this work, an investigation of thermal imaging in non-destructive tests to detect cracks in the inner volume of TBCs has been carried out. A novel algorithm based on gray gradient space histogram entropy (GGSHE), multi-threshold segmentation and stacked restricted Boltzmann machine (GEMSS) is proposed. GGSHE extracts the color and spatial features from the acquired infrared image, and then multi-threshold segmentation is used to segment the crack tentatively. A stacked restricted Boltzmann machine is adopted to diagnose the crack length as well as the position and density. Experiments are conducted to test the performance of GEMSS and other algorithms. In the theoretical experiments, it is found that the average accuracy of the proposed algorithm is 95.5% under crack position detection and 86.7% under crack density detection. Theoretical experimental results also reveal that GEMSS can achieve the highest F-score (0.85) under crack length evaluation among the algorithms. Experiments on the effect of image noise on the detection of the algorithm are also carried out and discussed. Coatings with different prepared crack lengths are fabricated and tested using our algorithm. Based on the results of this study, the proposed method shows good potential to evaluate internal cracks of TBCs in actual process.

INTERNAL SPECKLE PATTERN QUALITY ASSESSMENT AND OPTIMAL SELECTION OF VOXEL POINTS FOR DIGITAL VOLUME CORRELATION

Digital volume correlation (DVC) is a powerful experimental tool for quantitative 3D internal deformation measurement throughout the interior of materials or biological tissues. By comparing the volumetric images acquired at the reference and deformed states by a volumetric imaging facility (e.g., X-ray CT), DVC provides full-field displacements with subvoxel accuracy and full-field strain maps. When using DVC method for internal deformation measurement, the quality of internal speckle pattern of a test sample has an important impact on the accuracy and precision of the measured displacements. In this work, theoretical analysis and numerical simulation experiments are firstly performed to assess the quality of internal speckle patterns. The results show that the displacement measurement error of DVC is negative correlation with the sum of square of subvolume intensity gradient (SSSIG) of a subvolume. Thus, the SSSIG value of a subvolume can be used as a simple and effective metric for quality assessment of its internal speckle pattern. In practical DVC analyses, increasing the SSSIG parameter of a subvolume helps to improve the measurement accuracy and precision of DVC. Despite the SSSIG in a subvolume can be increased by enlarging its subvolume size, this simple approach, however, would lead to increased calculation burden. With a purpose to increase SSSIG without significantly increase the amount of calculation, this paper further proposes a method for optimal selection of calculation voxel points in subvolumes. Specifically, the voxel points with small gray gradients in a subvolume are excluded from DVC calculation to decrease the amount of calculation when increasing the size of the subvolume. The efficacy of the presented voxel point optimal selection method is validated by analyzing computer simulated and experimentally obtained volume images. Experimental results reveal that the proposed voxel point selection method is capable of reducing the calculation burden when increasing the subvolume size to increase the SSSIG parameters.

Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

Visualization of mercury percolation in porous hardened cement paste by means of X-ray computed tomography

Mercury intrusion porosimetry (MIP) has been extensively used for the pore structure characterization of porous materials, but the percolation of mercury in porous cement-based materials during MIP has never been comprehensively investigated before. Here we visually probe the mercury percolation in a porous hardened cement paste (HCP) using X-ray computed tomography (X-CT). Novel double surface coatings on the HCP samples before and after MIP tests by an epoxy resin are specifically designed. The first coating enables the one-directional penetration of mercury in the HCP samples during MIP, while the second coating can partially seal the release of the entrapped mercury. MIP tests are operated under different maximum applied pressures (10,000 and 30,000 psi) and equilibrium time (5, 10, and 30 s). The high X-ray attenuation of mercury enables the visual trace of the intrusion mercury percolation pathways and the entrapped mercury clusters in the HCP samples by X-CT. The position-dependent entrapped mercury clusters in the pores account for the gray value changes and gradients in the HCP samples after MIP. The MIP characteristics, such as, total intrusion volume, volume size distribution and mercury entrapment, change with the maximum applied pressure and equilibrium time, but the percolation pore size shows the consistent values. The findings of this work deepen the understandings of MIP for the microstructure characterization of porous materials.

Sub-pixel displacement measurement based on the combination of a gray wolf optimizer and gradient algorithm.

The digital speckle correlation method (DSCM) aims to measure the displacement of the interesting points by matching the subset around the same point between the undeformed image and the deformed image. It is an effective and powerful optical metrology method for deformation measurement. Considering that the gray wolf optimizer (GWO) is one of the most popular metaheuristic algorithms to calculate the unknown search spaces in the field of optical engineering, a sub-pixel displacement measurement technique based on the GWO and gradient algorithm is proposed. First, the zero-mean normalized cross correlation function is applied to analyze the correlation between the reference image and deformed image subsets. Second, by exploiting the global searching ability of the GWO algorithm, the initial integer pixel value is obtained and further viewed as the initialization displacement. Finally, the final sub-pixel displacement is generated by using a Barron gradient algorithm. Compared with the state-of-the-art methods on synthetic speckle images, the proposed method can effectively measure the displacement and deformation of rigid bodies. Furthermore, the experiments on the real images demonstrate the effectiveness of our presented framework.

Method of microscopic imaging quality evaluation for board lens based on image parameter extraction of pinhole imaging

The modulation transfer function area (MTFA) of pinhole imaging and the MTFA relative variation ratio were used as the image surface parameters to quantitatively evaluate the microscopic imaging quality of the board lens. The light spot images located in different fields of view positions were obtained by using the pinhole imaging method to extract the modulation transfer function (MTF) of microscopic imaging system for board lens. The MTF mathematical model of lens system for board lens was established, and the MTFA in sagittal direction and meridian direction of edge field of view as well as the MTFA relative variation ratio were extracted to measure the microscopic imaging definition and image surface flatness of the measured lens. An experiment which was for measuring the board lens was carried out, and the microscopic imaging performance of the measured lens was quantitatively evaluated by using the proposed model and method. The results show that the Lens 2 can obtain the optimal microscopic imaging quality. The definition value of average power spectrum value (APSV), the gray mean gradient (GMG) and the laplacian summation (LS) were calculated respectively from the RGB bitmap images captured by these measured lens. The results show that the maximum parameter value of the APSV, GMG and LS of Lens 2 bitmap images is equal to 2.720 2, 17.024 4 and 94.921 2, respectively. At the same time, the bitmap images have the highest definition, which is consistent with the image surface parameter evaluation results, indicating that the proposed method used in the quantitative evaluation of microscopic imaging performance of board lens is accurate and effective, which is of great significance and engineering value to improve the online image probe design of the on-line visual ferrograph (OLVF).

Target motion detection algorithm based on dynamic threshold

Realizing moving target detection through visual algorithms is a major branch of computer technology. Moving target detection has a wide range of applications in many fields such as video surveillance, unmanned driving, and behavior research. The current algorithm has been able to achieve moving target detection well in a static background, but the detection of multiple moving targets in a dynamic background is still a challenging research area. The main pain points are complex background, light changes, noise interference and Occlusion and real-time detection requirements, etc. Aiming at the detection of moving targets in complex scenes, a moving target detection algorithm based on dynamic threshold is proposed. The algorithm first uses the gray difference gradient of three adjacent frames of the video sequence to calculate the segmentation threshold of foreground moving targets to realize the dynamic threshold. Then use dynamic thresholds to achieve multi-target motion detection in complex backgrounds or scenes with changing lighting. Because the gradient change of adjacent frames of the scene is considered in the process of moving target detection and segmentation, and the gradient change is converted into a dynamic threshold to participate in moving target detection and segmentation, the algorithm can adapt to scenes with changing lighting. Experiments show that this algorithm is feasible for moving target detection in complex scenes, and has a lower error rate than that of traditional algorithms, and is more suitable for to complex or changing lighting scenes.

Simultaneous measurement of cloud microphysical parameters based on digital holographic interferometry

Investigation of cloud microphysical is of great significance in deepening the understanding of the radiation energy budget, water cycle process, and precipitation mechanism, and improving the scientificity and effectiveness of artificial precipitation. Especially under the action of turbulence, in addition to shear and inertia, the turbulence in the cloud will accelerate the collision of cloud droplets through vortex superposition. The above process will further complicate the cloud microphysical characters. At present, the methods of measuring cloud microphysical parameters based on light scattering, collision and imaging theories encounter bottlenecks: the inversion process needs to make the assumptions about cloud droplet spectrum and particle characteristics, the impact process will destroy particle characteristics, and the three-dimensional characteristics of cloud particles cannot be obtained. Because of its many advantages, such as fast, real-time, non-destructive, non-invasive, high-resolution, full-field optical measurement, etc., in-line digital holographic interferometry is considered as a new potential tool for the dynamical measurement of cloud microphysical property. In particular, the mutual interference between the particle image and twin image is small under far-field recording conditions. In this paper, the measurement method of the on-line digital holographic interferometry based on interference theory, combining optical information processing, depth of field compression, and gray gradient variance technology of fusion holograms, is investigated. This method, with a &lt;i&gt;z&lt;/i&gt;-axis position accuracy of 0.01 mm and system resolution of 2 μm, is employed for simultaneously and finely detecting the cloud droplet spectrum, cloud particle diameter, and number concentration. In the experiment, the liquid droplet with a median diameter of 3.9 μm, produced by the ultrasonic atomizer, is used as an example of the cloud particle. The measurement results are consistent with realistic scenario. By using a high speed charge coupled device or complementary metal oxide semiconductor camera, this method can solve the technical bottleneck of three-dimensional fine characteristics of cloud particle in airborne measurement by using cloud droplet spectrometer. It can provide effective support for the research of liquid water in the cloud, entrainment, condensation, collision, and temporal and spatial evolution laws. In addition, it has reference significance for the study of particle dynamics. Simultaneously, this method provides a feasible solution for the measurement of cloud in land-based and airborne platforms.