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Journal articles on the topic 'Digital image correlation'

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Published: 4 June 2021
Last updated: 7 July 2024

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1

McCormick, Nick, and Jerry Lord. "Digital Image Correlation." Materials Today 13, no. 12 (December 2010): 52–54. http://dx.doi.org/10.1016/s1369-7021(10)70235-2.

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2

Wilson, Anthony J., Bianca C. Chin, Vivian M. Hsu, Michael N. Mirzabeigi, and Ivona Percec. "Digital Image Correlation." Plastic and Reconstructive Surgery 135, no. 5 (May 2015): 869e—876e. http://dx.doi.org/10.1097/prs.0000000000001224.

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3

Hwang, Chi Hung, Wei-Chung Wang, Yung-Hsiang Chen, and Chih-Yen Chen. "OS2-11 Multiple-Camera Semi-circular Digital Image Correlation System for Monitoring Retaining Wall(Digital image correlation and its applications (3),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 30. http://dx.doi.org/10.1299/jsmeatem.2015.14.30.

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4

Fujimoto, Yasuhisa, Shuichi Arikawa, Riku Yoshida, Yohei Omoto, and Satoru Yoneyama. "OS2-13 Thermal Strain Measurement of Electronic Packaging Structure Using Highly Accurate Digital Image Correlation(Digital image correlation and its applications (4),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 32. http://dx.doi.org/10.1299/jsmeatem.2015.14.32.

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5

Khaja, Abdul Aziz, and Wael A. Samad. "Hybrid Digital Image Correlation." Journal of Engineering Mechanics 146, no. 4 (April 2020): 04020009. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001726.

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6

Xie, Huimin, and Yilan Kang. "Digital image correlation technique." Optics and Lasers in Engineering 65 (February 2015): 1–2. http://dx.doi.org/10.1016/j.optlaseng.2014.07.010.

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7

Arikawa, Shuichi, Riku Yoshida, Satoru Yoneyama, Yasuhisa Fujimoto, and Yohei Omoto. "OS2-12 A Method for Eliminating Periodical Error for Highly Accurate Measurement in Digital Image Correlation(Digital image correlation and its applications (4),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 31. http://dx.doi.org/10.1299/jsmeatem.2015.14.31.

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8

Kang, Huimin, zhende Hou, and Cheng Yue. "OS2-15 Measurement of Bending Response of Bone in an Electric Field Using Digital Image Correlation(Digital image correlation and its applications (4),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 34. http://dx.doi.org/10.1299/jsmeatem.2015.14.34.

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9

Karino, Keiji, and Takuma Matsuo. "OS2-9 Non-contact Deflection Measurement for Health Monitoring of Overpass by Using Digital Image Correlation Method(Digital image correlation and its applications (3),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 28. http://dx.doi.org/10.1299/jsmeatem.2015.14.28.

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10

Yang, J., and K. Bhattacharya. "Combining Image Compression with Digital Image Correlation." Experimental Mechanics 59, no. 5 (January 18, 2019): 629–42. http://dx.doi.org/10.1007/s11340-018-00459-y.

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11

Neggers, J., B. Blaysat, J. P. M. Hoefnagels, and M. G. D. Geers. "On image gradients in digital image correlation." International Journal for Numerical Methods in Engineering 105, no. 4 (July 14, 2015): 243–60. http://dx.doi.org/10.1002/nme.4971.

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12

Sjödahl, Mikael. "Gradient Correlation Functions in Digital Image Correlation." Applied Sciences 9, no. 10 (May 24, 2019): 2127. http://dx.doi.org/10.3390/app9102127.

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The performance of seven different correlation functions applied in Digital Image Correlation has been investigated using simulated and experimentally acquired laser speckle patterns. The correlation functions were constructed as combinations of the pure intensity correlation function, the gradient correlation function and the Hessian correlation function, respectively. It was found that the correlation function that was constructed as the product of all three pure correlation functions performed best for the small speckle sizes and large correlation values, respectively. The difference between the different functions disappeared as the speckle size increased and the correlation value dropped. On average, the random error of the combined correlation function was half that of the traditional intensity correlation function within the optimum region.
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13

Sakaue, Kenich, Susumu Ohki, and Shinichi Suzuki. "OS2-6 Discussion on Energy Allocation problem into Branch Cracks by using Stationary Bifurcated Crack and Digital Image Correlation(Digital image correlation and its applications (2),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 25. http://dx.doi.org/10.1299/jsmeatem.2015.14.25.

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14

Tomičevć, Zvonimir, François Hild, and Stéphane Roux. "Mechanics-aided digital image correlation." Journal of Strain Analysis for Engineering Design 48, no. 5 (June 11, 2013): 330–43. http://dx.doi.org/10.1177/0309324713482457.

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15

Shen, Huan, Peize Zhang, and Xiang Shen. "Kernel based Digital Image Correlation." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 3 (March 31, 2015): 261–72. http://dx.doi.org/10.14257/ijsip.2015.8.3.24.

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16

Yang, J., and K. Bhattacharya. "Augmented Lagrangian Digital Image Correlation." Experimental Mechanics 59, no. 2 (December 6, 2018): 187–205. http://dx.doi.org/10.1007/s11340-018-00457-0.

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17

Mudassar, Asloob Ahmad, and Saira Butt. "Improved Digital Image Correlation method." Optics and Lasers in Engineering 87 (December 2016): 156–67. http://dx.doi.org/10.1016/j.optlaseng.2015.10.002.

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18

Curt, J., M. Capaldo, F. Hild, and S. Roux. "Optimal digital color image correlation." Optics and Lasers in Engineering 127 (April 2020): 105896. http://dx.doi.org/10.1016/j.optlaseng.2019.105896.

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19

Pan, Bing, and Long Tian. "OS2-8 Real-time, Non-contact and Targetless Measurement of Vertical Deflection of Bridges using Off-axis Digital Image Correlation(Digital image correlation and its applications (3),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 27. http://dx.doi.org/10.1299/jsmeatem.2015.14.27.

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20

Chen, Terry Yuan-Fang, and Ren-Shiang Lu. "OS2-2 Development of a Hybrid Infrared Image Correlation Technique to deformation measurement of Composites(Digital image correlation and its applications (1),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 21. http://dx.doi.org/10.1299/jsmeatem.2015.14.21.

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21

Chen, Wei, Zhenyu Jiang, and Liqun Tang. "OS2-3 A Comparison of Accuracy and Convergence Capability between IC-GN Algorithm and FA-NR Algorithm in Digital Image Correlation(Digital image correlation and its applications (1),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 22. http://dx.doi.org/10.1299/jsmeatem.2015.14.22.

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22

Kojima, Yuka, Hiroshi Hohjo, Takashi Ohta, and Hiroaki Makino. "OS2-14 Visualization of Non-uniform Microscopic Distribution of Thermal Strains in Glass-fiber-cloth/epoxy Laminates by Digital Image Correlation Method(Digital image correlation and its applications (4),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 33. http://dx.doi.org/10.1299/jsmeatem.2015.14.33.

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23

Shin, Bumshick, and KyuHan Kim. "Analysis of Wave-Induced Current Using Digital Image Correlation Techniques." Journal of Sensors 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/1784507.

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Recently, advancement of digital image techniques and communications technology has enabled the application of existing images for scientific purposes. Furthermore, both quantitative and qualitative analyses of images have become possible through image processing such as transmit/storage of digital image data and image rectification. In this study, a coast having representative characteristics of east coast of Korea was selected with having erosion in winter, and the sedimentation in summer takes place repeatedly. Three-dimensional hydraulic model test was conducted to analyze its outcomes by a digital image correlation technique in order to understand the wave-induced current affecting the sediment transport. For this study, images filmed by the high-sensitive and high-resolution video camera were converted into stopped images of regular intervals and then those converted images were used for the following procedure to analyze flow and velocity into digital coordinates. The outcomes from interpretation of images filmed by the high-sensitive and high-resolution video camera can be utilized as a very useful analysis method for appreciating the generation mechanism and movement route of longshore current and rip current.
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24

Forsey, A., and S. Gungor. "Demosaicing images from colour cameras for digital image correlation." Optics and Lasers in Engineering 86 (November 2016): 20–28. http://dx.doi.org/10.1016/j.optlaseng.2016.05.006.

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25

Atkinson, Devan, and Thorsten Hermann Becker. "Stereo Digital Image Correlation in MATLAB." Applied Sciences 11, no. 11 (May 26, 2021): 4904. http://dx.doi.org/10.3390/app11114904.

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Digital Image Correlation (DIC) has found widespread use in measuring full-field displacements and deformations experienced by a body from images captured of it. Stereo-DIC has received significantly more attention than two-dimensional (2D) DIC since it can account for out-of-plane displacements. Although many aspects of Stereo-DIC that are shared in common with 2D DIC are well documented, there is a lack of resources that cover the theory of Stereo-DIC. Furthermore, publications which do detail aspects of the theory do not detail its implementation in practice. This literature gap makes it difficult for newcomers to the field of DIC to gain a deep understanding of the Stereo-DIC process, although this knowledge is necessary to contribute to the development of the field by either furthering its capabilities or adapting it for novel applications. This gap in literature acts as a barrier thereby limiting the development rate of Stereo-DIC. This paper attempts to address this by presenting the theory of a subset-based Stereo-DIC framework that is predominantly consistent with the current state-of-the-art. The framework is implemented in practice as a 202 line MATLAB code. Validation of the framework shows that it performs on par with well-established Stereo-DIC algorithms, indicating it is sufficiently reliable for practical use. Although the framework is designed to serve as an educational resource, its modularity and validation make it attractive as a means to further the capabilities of DIC.
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26

McCormick, Nick, and Jerry Lord. "Digital image correlation for structural measurements." Proceedings of the Institution of Civil Engineers - Civil Engineering 165, no. 4 (November 2012): 185–90. http://dx.doi.org/10.1680/cien.11.00040.

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27

Murray, Chris A., Neil A. Hoult, and W. Andy Take. "Dynamic measurements using digital image correlation." International Journal of Physical Modelling in Geotechnics 17, no. 1 (March 2017): 41–52. http://dx.doi.org/10.1680/jphmg.15.00055.

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28

Cofaru, Corneliu, Wilfried Philips, and Wim Van Paepegem. "Pixel-level robust digital image correlation." Optics Express 21, no. 24 (November 27, 2013): 29979. http://dx.doi.org/10.1364/oe.21.029979.

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29

Withers, Philip J. "Strain Measurement by Digital Image Correlation." Strain 44, no. 6 (December 2008): 421–22. http://dx.doi.org/10.1111/j.1475-1305.2008.00556.x.

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30

Baldi, A. "Digital Image Correlation and Color Cameras." Experimental Mechanics 58, no. 2 (October 13, 2017): 315–33. http://dx.doi.org/10.1007/s11340-017-0347-2.

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31

SU, Yong, QingChuan ZHANG, and XiaoPing WU. "Progress in digital image correlation method." SCIENTIA SINICA Physica, Mechanica & Astronomica 48, no. 9 (August 9, 2018): 094604. http://dx.doi.org/10.1360/sspma2018-00156.

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32

Wang, Min, Hao Wang, and Yuwan Cen. "High-speed digital-image correlation method." Optics Letters 34, no. 13 (June 22, 2009): 1955. http://dx.doi.org/10.1364/ol.34.001955.

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33

Dupré, J. C., M. Bornert, L. Robert, and B. Wattrisse. "Digital image correlation: displacement accuracy estimation." EPJ Web of Conferences 6 (2010): 31006. http://dx.doi.org/10.1051/epjconf/20100631006.

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34

Tavares, Paulo J., Tiago Ramos, Daniel Braga, Mario A. P. Vaz, and Pedro Miguel Guimarães Pires Moreira. "SIF determination with digital image correlation." International Journal of Structural Integrity 6, no. 6 (December 7, 2015): 668–76. http://dx.doi.org/10.1108/ijsi-03-2015-0010.

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Purpose – Hybrid methods, wherefore numerical and experimental data are used to calculate a critical parameter, have been used for several years with great success in Experimental Mechanics and, in particular, in fracture mechanics. The purpose of this paper is to report on the comparison of the strain field from numerical modelling forecasts against the experimental data obtained with the digital image correlation method under Mode II loading in fatigue testing. The numerical dual boundary element method has been established in the past as a very reliable method near singular regions where stresses tend to grow abruptly. The results obtained from the strain data near the crack tip were used in Williams expansion and agree fairly well with both the numerical results and the analytical solution proposed for pure Mode II testing. Design/methodology/approach – The work presented in this note is experimental. The proposed methodology is of an hybrid experimental/numerical nature in that a numerical stress intensity factor calculation hinges upon a stress field obtained with an image method. Findings – The obtained results are an important step towards the development of a practical tool for crack behaviour prediction in fatigue dominated events. Research limitations/implications – The results also stress the necessity of improving the experimental techniques to a point where the methods can be applied in real-life solicitations outside of laboratory premises. Originality/value – Although several research teams around the globe are presently working in this field, the present research topic is original and the proposed methodology has been presented initially by the research team years ago.
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35

Pan, B. "Recent Progress in Digital Image Correlation." Experimental Mechanics 51, no. 7 (November 13, 2010): 1223–35. http://dx.doi.org/10.1007/s11340-010-9418-3.

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36

Jones, E. M. C., E. C. Quintana, P. L. Reu, and J. L. Wagner. "X-Ray Stereo Digital Image Correlation." Experimental Techniques 44, no. 2 (August 26, 2019): 159–74. http://dx.doi.org/10.1007/s40799-019-00339-7.

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37

Neggers, J., J. P. M. Hoefnagels, M. G. D. Geers, F. Hild, and S. Roux. "Time-resolved integrated digital image correlation." International Journal for Numerical Methods in Engineering 103, no. 3 (March 26, 2015): 157–82. http://dx.doi.org/10.1002/nme.4882.

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38

Lin, Zhe, Tian Cai, and Yanfeng Wang. "Parallel reliability-guided algorithm for digital image correlation." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 12 (December 1, 2019): 729–37. http://dx.doi.org/10.1784/insi.2019.61.12.729.

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Digital image correlation is a non-contact optical method for measuring the displacement and strain on the surface of a material. The existing reliability-guided digital image correlation (RG-DIC) method is stable and reliable for a single image but it still needs a large calculational resource for a sequence of images. Due to the decorrelation effect, the reference image must be replaced several times to correct the measurement results for an image sequence involving a large deformation or a discontinuous deformation. Since the process must be executed sequentially, image by image, the total time required is often unacceptably large when the image sequence is long. The challenge is to find a way of improving the speed while retaining calculational reliability and measurement accuracy, which are important for the practical application of DIC. To address this problem, an improved method is proposed in this paper. The parallel bottleneck caused by the decorrelation effect is solved through improving the parallelism to increase the processing speed. This approach can be used to calculate the strain field of the surface of the material in cases of discontinuous deformation, such as in the area near to a crack. Compared with existing methods, this method not only retains the calculational reliability but also greatly improves calculation speed, especially on current multi-core computing platforms.
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39

Chen, Lin, Rong Sheng Lu, Yan Qiong Shi, and Jian Sheng Tian. "A Differential Evolution Stereo Matching Method in Digital Image Correlation." Key Engineering Materials 625 (August 2014): 297–304. http://dx.doi.org/10.4028/www.scientific.net/kem.625.297.

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Stereo matching is widely used in three-dimensional (3D) reconstruction, stereo machine vision and digital image correlation. The aim of stereo matching process is to solve the well-known correspondence problem, which tries to match points or features from one image with the same points or features in another image from the same 3D scene. There are two basic ways, correlation-based and feature-based, are used to find the correspondences between two images. The correlation-based way is to determine if one location in one image looks/seems like another in another image, and the feature-based way to find if a subset of features in one image is similar in the another image. In stereo matching, a simple algorithm is to compare small patches between two rectified images by correlation search. For the pair images acquired from two cameras inevitably exists some rotation transformation, the algorithm first runs a preprocessing step to rectify the images with the epipolar rectification to simplify the problem of finding matching points between images. The epipolar rectification is to determine a transformation of each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axes. It will lead the loss of gray information of images. The effect is dependent on the amount of angle. When the angle is big enough, the correlation search may yield error results because of retrograded correlation effect. In order to solve the problem, the paper presents an improved stereo matching algorithm with differential evolution to solve the correspondence problem. Our method doesn’t need to runs the preprocessing step to rectify the images with the epipolar rectification. It uses a differential evolution algorithm to minimize the correlation function which contains the angle information after acquiring the epipoar geometry constraint of two image pairs. Then it utilizes a flood-fill algorithm to search correspondence sub-region in the area around the epipolar line. The flood-fill algorithm can overcome the problem of the traditional row-column scanning search method, which will encounter boundary barrier where exists concave polygons or cavities. The Experimental results show that the proposed method can be easily implemented in stereo matching without loss of information of image features with large rotation angle transformation. In the paper, we will introduce the stereo matching principle and its algorithms, including the differential evolution algorithm for finding the correspondences with large rotation transformation between stereo image pairs and the flood-fill traversal strategy for matching large area with complex concave polygons or cavities. In the end of the paper, some experimental results will be given to illustrate the method effectiveness. Keywords: digital image correlation, stereo matching algorithm, epipolar geometry, flood fill algorithm, differential evolution, rotation angle
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40

López-Ávila, Luis Felipe, and Josué Álvarez-Borrego. "Fractional Fourier Transform for Digital Image Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (January 9, 2020): 2054025. http://dx.doi.org/10.1142/s0218001420540257.

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In the image recognition field, there are several techniques that allow identifying patterns in digital images, correlation being one of them. In a correlation, you have to obtain an output plane that is as clean as possible. To measure the sharpness of the correlation peak and the cleanliness of the output plane, a performance metric called Peak to Correlation Energy (PCE) is used. In this paper, the fractional correlation is applied to recognize real phytoplankton images. This fractional correlation guarantees a higher PCE compared to the conventional correlation. The results of PCE are two-orders of magnitude higher than those obtained with the conventional correlation and manage to identify 91.23% of the images, while the conventional correlation only manages to identify 87.42% of them. This methodology was tested using images in salt and pepper or Gaussian noise, and the fractional correlation output plane always is cleaner and generates a better-defined correlation peak when compared with the classical correlation.
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41

Chen, Jiangping, and Weijun Tao. "Gradient-Based Automatic Exposure Control for Digital Image Correlation." Sustainability 15, no. 2 (January 7, 2023): 1149. http://dx.doi.org/10.3390/su15021149.

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Digital image correlation (DIC) is widely used in material experiments such as ores; the quality of a speckle image directly affects the accuracy of the DIC calculation. This study aims to acquire high-quality speckle pattern images and improve the calculation accuracy and stability. A gradient-based image quality metric was selected to evaluate the image quality, and its validity was verified by a rigid body experiment and a numerical experiment. Based on the maximum image quality metric, an automatic exposure control algorithm and the control procedure were proposed to obtain the optimal exposure time. Finally, nine sets of images with different poses and illuminations were captured, and displacement and strain fields were calculated at the fixed exposure time and the optimized exposure time. The results of the rigid-body motion experiment show that the calculated data at the optimized exposure time is smoother and less noisy, and the error is smaller, which verifies the effectiveness of the exposure control procedure and its algorithm and improves the accuracy and stability of DIC calculation.
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42

Barros, Francisco, Pedro J. Sousa, Paulo J. Tavares, and Pedro MGP Moreira. "Digital image correlation through image registration in the frequency domain." Journal of Strain Analysis for Engineering Design 53, no. 8 (September 28, 2018): 575–83. http://dx.doi.org/10.1177/0309324718802705.

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The present work analyses displacement field computations for several digital image correlation test cases obtained through the application of image registration in the Fourier domain. The applied method relies on the properties of the discrete Fourier transform with regard to the cross-correlation function to find the rigid displacement between two subsets. The computed displacement fields were compared to their analogous values obtained through a commercial digital image correlation code. The test cases cover both two-dimensional and three-dimensional digital image correlation and make use of the ability of the developed method to calculate the translation and rotation between subsets from images at different loading states, as well as its capacity to match subsets from two cameras of a stereo rig. The presented analysis aims to contribute to the investigation of the use of correlation in the frequency domain as the basis for an alternative to conventional digital image correlation which could provide advantages in terms of robustness and efficiency, particularly in field measurements, where the lack of control over lighting and other factors impacting image quality requires a system which is optimised towards low dependence on the optimal quality of these conditions.
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43

Koyama, Hiroto, and Eisaku Umezaki. "OS2-7 Behavior of Ultraviolet-Curable Resin during Curing(Digital image correlation and its applications (2),OS2 Digital image correlation and its applications,MEASUREMENT METHODS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 26. http://dx.doi.org/10.1299/jsmeatem.2015.14.26.

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44

Jia, Zhaoyang, and Guangxue Chen. "Study on Digital Image Inpainting Method Based on Multispectral Image Decomposition Synthesis." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (October 11, 2018): 1954004. http://dx.doi.org/10.1142/s0218001419540041.

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The paper analyzes the image inpainting problem of damaged Painting Arts for high fidelity images reproduction, and a digital image inpainting method based on multispectral image decomposition synthesis is proposed. Firstly, multi-channel images of Painting Arts are obtained by multispectral technology. Then, a polynomial regression method based on principal component is used to reconstruct the spectral image. The reconstructed image is decomposed by VO image decomposition model. During the inpainting process, the channel correlation of the structure image and the texture image of multispectral image is effectively removed. The digital image inpainting is performed respectively. Finally, the digital inpainted image is obtained by synthesis. The experimental results show that the digital image inpainting based on multispectral image decomposition synthesis reduces the problem of low image inpainting accuracy caused by the correlation between the color components in the traditional digital image inpainting process, and reduces the mismatch of the inpainting image. Appearance of pseudo color of inpainting image is reduced. MSE of multispectral images inpainting qualities is 2.7951 and PSNR of multispectral images inpainting qualities is 44.1681, so it is superior to traditional image inpainting algorithm. It provides a reliable basis for digital inpainting, digital archives and high fidelity replication of defective Painting Arts.
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45

Oh, Ju Hwan, Jong Min Kim, and Cheol Soo Lee. "Digital Image Correlation using Image Calibration under Restricted Environment." Journal of the Korean Society of Manufacturing Technology Engineers 30, no. 4 (August 31, 2021): 323–31. http://dx.doi.org/10.7735/ksmte.2021.30.4.323.

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46

Besnard, Gilles, Hugo Leclerc, François Hild, Stéphane Roux, and Nicolas Swiergiel. "Analysis of image series through global digital image correlation." Journal of Strain Analysis for Engineering Design 47, no. 4 (April 27, 2012): 214–28. http://dx.doi.org/10.1177/0309324712441435.

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47

Pan, Bing. "Reliability-guided digital image correlation for image deformation measurement." Applied Optics 48, no. 8 (March 4, 2009): 1535. http://dx.doi.org/10.1364/ao.48.001535.

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48

Vaitkunas, Tomas, Paulius Griskevicius, and Adi Adumitroaie. "Peridynamic Approach to Digital Image Correlation Strain Calculation Algorithm." Applied Sciences 12, no. 13 (June 28, 2022): 6550. http://dx.doi.org/10.3390/app12136550.

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Digital image correlation is an experimental optical non-contact full field displacement and strain evaluation method based on the surface subsets tracking with photo cameras, digital images processing, and numerical computation. However, the full field of strain computation is a challenging problem, mainly because the displacement field to be differentiated is not continuous, individual subsets are tracked by the optical digital image correlation system. Moreover, the numerical differentiation can also amplify the noise of the displacement field inducing thus strain errors when the displacement data are poor. The peridynamics theory (which equations are cast in terms of spatial integrals of displacements, instead of spatial derivatives in the classical continuum mechanics) based algorithm is considered in this study and applied for the experimental digital image correlation displacement field to analyze possible peridynamic differentiation method advantages. A strains convergence analysis between the digital image correlation and peridynamic differentiation methods is done in this study. The integro-differential strain calculation as an alternative method is validated against digital image correlation and finite element simulation strain fields. It is also shown that the digital image correlation, a noisy displacement field, still provides an accurate and low level noise strain evaluation based on the proposed method.
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49

Berveglieri, Adilson, and Antonio Tommaselli. "Reconstruction of Cylindrical Surfaces Using Digital Image Correlation." Sensors 18, no. 12 (November 29, 2018): 4183. http://dx.doi.org/10.3390/s18124183.

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A technique for the reconstruction of cylindrical surfaces using optical images with an extension of least squares matching is presented. This technique is based on stereo-image acquisition of a cylindrical object, and it involves displacing the camera following the object length. The basic concept behind this technique is that variations in the camera viewpoint over a cylindrical object produce perspective effects similar to a conic section in an image sequence. Such parallax changes are continuous and can be modelled by a second-order function, which is combined with an adaptive least squares matching (ALSM) for the 3D object reconstruction. Using this concept, a photogrammetric intersection with only two image patches can be used to model a cylindrical object with high accuracy. Experiments were conducted with a cylinder on a panel with coded targets to assess the 3D reconstruction accuracy. The accuracy assessment was based on a comparison between the estimated diameter and the diameter directly measured over the cylinder. The difference between the diameters indicated an accuracy of 1/10 mm, and the cylindrical surface was entirely reconstructed.
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50

Liao, Yi-Hong, Manzhu Xu, and Song Zhang. "Digital image correlation assisted absolute phase unwrapping." Optics Express 30, no. 18 (August 24, 2022): 33022. http://dx.doi.org/10.1364/oe.470704.

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This paper presents an absolute phase unwrapping method for high-speed three-dimensional (3D) shape measurement. This method uses three phase-shifted patterns and one binary random pattern on a single-camera, single-projector structured light system. We calculate the wrapped phase from phase-shifted images and determine the coarse correspondence through the digital image correlation (DIC) between the captured binary random pattern of the object and the pre-captured binary random pattern of a flat surface. We then developed a computational framework to determine fringe order number pixel by pixel using the coarse correspondence information. Since only one additional pattern is used, the proposed method can be used for high-speed 3D shape measurement. Experimental results successfully demonstrated that the proposed method can achieve high-speed and high-quality measurement of complex scenes.
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