Relevant bibliographies by topics / Digital image correlation

Academic literature on the topic 'Digital image correlation'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Digital image correlation"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Digital image correlation"

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Mosayebi, Mahshad. "Digital Laser Speckle Image Correlation." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2131.

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This thesis examines the feasibility of combining Digital Image Correlation (DIC) with laser speckle based methods to form a new hybrid deformation measurement method called Digital Laser Speckle Image Correlation (DilSIC). Consequently, this method does not require any sample preparation and allows for the measurement of displacement of micro structures in addition to large displacements. In this technique, a coherent 30mW-632nm laser beam is expanded with 40X lens and then illuminated on the target surface to produce a fine, homogenous laser speckle pattern. Images were captured before and after deformation due to external load and the whole field displacement and strain were determined by the DIC method. This technique could measure displacement less than 30-μm with high accuracy when a 120mm × 80mm area of the surface was inspected. Up to 10% strain was measured by this technique with high accuracy during the whole range. Eventually the sub-surface crack was located successfully, which is a revolutionary achievement in NDT optical methods. This method was tested in different material, with different roughness. Aluminum sheet and rubber material were used mostly. This method could broaden the capability of displacement measurement and subsurface crack detection in wide range of materials.
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Hartley, David Andrew. "Image correlation using digital signal processors." Thesis, Liverpool John Moores University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304465.

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Newberry, Shawn. "Laser Speckle Patterns with Digital Image Correlation." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2885.

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Digital Laser Speckle Image Correlation (DiLSIC) is a technique that utilizes a laser generated speckle pattern with Digital Image Correlation (DIC). This technology eliminates the need to apply an artifact speckle pattern to the surface of the material of interest, and produces a finer speckle pattern resulting in a more sensitive analysis. This investigation explores the parameters effecting laser speckle patterns for DIC and studies DiLSIC as a tool to measure surface strain and detect subsurface defects on pressure vessels. In this study a 632.8 nm 30 mW neon-helium laser generated the speckle pattern by passing through the objective end of an objective lens. All experiments took place in a lab setting on a high performance laminar flow stabilizer optical table.This investigation began with a deeper look at the camera settings that effect the effectiveness of using laser speckles with DIC. The first studies were concentrated on the aperture size (f-stop), shutter speed, and gain (ISO) of the camera. Through a series of zero-correlation studies, translation tests, and settings studies, it was discovered that, much like white light DIC, an increased gain allowed for more noise and less reliable measurements when using DiLSIC. It was shown that the aperture size and shutter speed will largely depend on the surface composition of the material, and that these factors should be investigated with each new sample of different surface finish.To determine the feasibility of using DiLSIC on pressure vessels two samples were acquired. The first was a standard ASTM filament wound composite pressure vessel (CPV) which had an upper load limit of 40 psi. The second was a plastic vessel that had internal subsurface defects added with the use of an air pencil grinder. Both vessels were put under a pressure load with the use of a modified air compressor that allowed for multiple loading cycles through the use of a pressure relief valve. The CPV was mapped out in 10-degree increments between the 90° and 180° markings that were on the pressure vessel, occurring in three areas, each one inch apart. The CPV had a pressure load applied to at 10, 20, 30,and 40 psi. DiLSIC was able to measure increasing displacement with increased loading on the surface of the CPV, however with a load limit of 40 psi no strains were detected. The plastic vessel had known subsurface defects, and these areas were the focus of the investigation. The plastic vessel was loaded with a pressure load at 5, 10, 12, 15, 17, and 20 psi. The 5 psi loaded image was used as a reference image for the correlation and decorrelation consistently occurred at 20 psi. This investigation proved that DiLSIC can detect and locate subsurface defects through strain measurement. The results were verified with traditional white light DIC, which also showed that the subsurface defects on pressure vessels were detectable. The DIC and DiLSIC results did not agree on maximum strain measurement, with the DiLSIC prediciting much larger strains than traditional DIC. This is due to the larger effect out-of-plane displacement has on DiLSIC. DiLSIC was able to detect subsurface defects on a pressure vessel. The median measured hoop strain was in agreement for DiLSIC, DIC and the predicted hoop strain for a wall thickness of 0.1 inches. However, DiLSIC also produced unreliable maximum strain measurements. This technique shows potential for future applications, but more investigations will be needed to implement it for industrial use. A full investigation into the parameters surrounding this technique, and the factors that contribute the most to added noise and unreliability should be conducted. This technology is being developed by multiple entities and shows promising results, and once further advanced could be a useful tool for rapid surface strain measurement and subsurface defect detection in nondestructive evaluation applications. Therefore, it is recommended to continue further investigations into this technology and its applications.
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Liang, Yiming. "Analysis of Paperboard Performance using Digital Image Correlation." Thesis, KTH, Hållfasthetslära, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277799.

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The performance of paperboard materials in packaging application has been investigated and evaluated for a long time. This is because it plays a decisive role for product protection and decoration in packaging applications. Potential damages during transportation sometimes affect the consistency of the performance. Therefore, the capability of the material to resist these external disturbances was of interest. A multiply paperboard was chosen as the experimental material. The analysis conducted in this thesis aimed to reveal the tensile behavior in the cross-machine direction (CD) of the material against various kinds of local or global changes. The changes included global and local climate variations, cutouts, and regional weakening and strengthening, which were applied during the intervals between preloading and reloading. The digital image correlation (DIC) analysis computed the time-varying strain fields from the gray level information contained in the recorded videos of loading processes.  The generated strain fields were imported to post analysis. Comparison between comparable stages (two stages with the same average strain value from different loading sections) was considered as the scheme of isolating the influences of the changes and investigating them individually. The cosine image similarity method and the eigenface algorithm were used to validate this scheme, while the directional average calculation and the strain field compensation method were introduced to realize the isolation. The differences between the front and back outer plies of the paperboard sheets were detected as individual. Moreover, both global and local climate changes were affecting the strain distributions of the specimens proportionally on account of the moisture ratio within the material. In addition, the invisible mechanical weakening and strengthening were captured evidently with the analysis, which caused strain concentrations due to the uneven distribution of expansion capability. The relaxation and bending in unloading processes were two of the primary disturbing factors within all the deformed specimens, which were related to time and bending direction, correspondingly.
Egenskaperna hos kartongmaterial för förpackningstillämpningar har varit ett ämne att undersökning under lång tid. Detta för att dessa egenskaper spelar en avgörande roll som produktskydd och dekorativ utformning i mängde av tillämpningar. Potentiella skador under transport påverkar bland annat materialets tillförlitlighet och prestandard. Därför är det aktuellt att undersöka samt förstå materialets förmåga att motstå yttre störningar. Experimentmaterialet som användes bestod av en typ av flerskiktskartong. Analyser som utfördes i denna avhandling har syfte att identifiera de mekaniska förändringarna i materialets dragegenskaper i tvärsmaskin-riktningen (CD) på grund av olika lokala eller globala förändringar. Förändringarna innefattar både globala och lokala klimatvariationer, utskärningar, och lokala försvagningar samt förstärkningar. Dessa förändringar infördes vid intervallet mellan på- och avlastning. Den digital bildkorrelations analys (DIC) användes för att beräknade de tidsvarierande töjningsfälten från den grånivåinformationen i som registrerades med hjälp av inspelade videor under belastningen  Den genererade töjningsfälten importerades för vidare analys. Två tillstånd med liknande medelvärde av töjningsnivån från olika delar av belastningen jämfördes, detta för att isolera påverkan av förändringarna och undersöka dem individuellt. Två olika metoder för jämförelse av bilderna (cosine image similarity och eigenface algorithm) användes för att validera analysschemat, där riktning-medelvärdesberäkningar och töjningsfälts kompensations-metoden användes för att realisera dessa isoleringar. Enstaka skillnader upptäcktes mellan de främre och bakre ytskikten på kartongarken. Dessutom påverkades töjningsfördelningarna för proverna både av den globala och lokala klimatförändringar på grund av fukttillståndet i materialet. Vidare kan de osynliga mekaniska försvagningar och förstärkningar tydligt fångas med de utförda analyserna, vilket ledde till töjningskoncentrationers uppkomst på grund av det inhomogena expansions-förmåga hos arket. Relaxationen och böjningen vid avlastning relaterade till tid och böjningsförmåga var två av de primära faktorerna som påverkade analysens kvalité.
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Nageswaran, Ashok R. "Deformation Analysis of Soft Tissues by Digital Image Correlation." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1233614556.

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Shrestha, Shashi Shekhar. "Evaluation of Composite Adhesive Bonds Using Digital Image Correlation." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/theses/1648.

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Advanced composite materials are widely used for many structural applications in the aerospace/aircraft industries today. Joining of composite structures using adhesive bonding offers several advantages over traditional fastening methods. However, this technique is not yet employed for fastening the primary structures of aircrafts or space vehicles. There are several reasons for this: There are not any reliable non-destructive evaluation (NDE) methods that can quantify the strength of the bonds, and there are no certifications of quality assurance for inspecting the bond quality. Therefore, there is a significant need for an effective, reliable, easy to use NDE method for the analysis of composite adhesive joints. This research aimed to investigate an adhesively bonded composite-aluminum joints of variable bond strength using digital image correlation (DIC). There are many future possibilities in continuing this research work. As the application of composite materials and adhesive bond are increasing rapidly, the reliability of the composite structures using adhesive bond should quantified. Hence a lot of similar research using various adhesive bonds and materials can be conducted for characterizing the behavior of adhesive bond. The results obtained from this research will set the foundation for the development of ultrasonic DIC as a nondestructive approach for the evaluation of adhesive bond line.
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Karimian, Seyed Fouad. "Evaluation of Adhesive Joints with Ultrasonic Digital Image Correlation." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/theses/2054.

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Increasing use of composite materials in industry brings the need for newer and more practical methods to evaluate them. Widespread use of composite materials heavily depends on the manufacturer’s ability to unquestionably ensure its safety, given how much the user trusts them. Non-Destructive Evaluation (NDE) can be used to evaluate adhesive bondline health. This thesis employs Digital Image Correlation (DIC) method, one of the known methods in NDE, and combines it with an embedded speckle pattern in order to obtain valuable information from within the adhesive bondline. By recording the movement of the speckles and analyzing their behavior according to DIC algorithms, a strain map of the adhesive is drawn. An adhesive strain map helps find defects that might be out of sight using conventional NDE methods. This thesis discusses different possible materials to be used as the speckle pattern and chooses the one shows better results based on different criteria. Then employing the material, it records the speckle pattern using optical and ultrasonic methods to draw a strain map. By analyzing the obtained strain maps, defects within the bondline are revealed.
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Sapounas, D. "A novel approach to rotation invariant correlation." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283306.

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Gubbels, Wade. "3-D Digital Image Correlation using a single color-camera." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50491.

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Digital Image Correlation (DIC) is an optical and numerical method capable of accurately providing full-field, two-dimensional (2-D) and three-dimensional (3-D) surface displacements and strains. 3-D DIC is typically done using two cameras that view the measured object from differing oblique directions. The measured images are independent and must be spatially connected using a detailed calibration procedure. This places a large demand on the practitioner, the optical equipment and the computational method. A novel approach is presented here where a single color-camera is used in place of multiple monochrome cameras. The color-camera measures three independent Red-Green-Blue (RGB) color-coded images. This feature greatly reduces the scale of the required system calibrations and spatial computations because the color images are physically aligned on the camera sensor. The in-plane surface displacements are obtained by performing traditional 2-D DIC in a single color. The out-of-plane information is obtained by a second 2-D DIC analysis and triangulation using oblique illumination from a differently colored light source. Further, the camera perspective errors associated with out-of-plane displacements can independently be measured during this second DIC analysis of the oblique illumination pattern. The 3-D Digital Image Correlation is completed by combining the 2-D correlations for each color. The design and creation of an example apparatus is described here. Experimental results show that the single-camera method can measure 3-D displacements with to within 1% error, with precision of the in-plane and out-of-plane measurements being consistently less than 0.04 and 0.12 pixels, respectively.
Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate
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Adapa, Sujatha. "Evaluation of friction stir weld samples using digital image correlation /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1136092291&sid=5&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Thesis (M.S.)--Southern Illinois University Carbondale, 2006.
"Department of Mechanical Engineering and Energy Processes." Includes bibliographical references (leaves 70-74). Also available online.
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Books on the topic "Digital image correlation"

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Merzkirch, Matthias. Mechanical Characterization Using Digital Image Correlation. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84040-2.

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Lin, Ming-Tzer, Cosme Furlong, and Chi-Hung Hwang, eds. Advancement of Optical Methods & Digital Image Correlation in Experimental Mechanics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59773-3.

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G, Bohorfoush Anthony, ed. Interpretation of ERCP: With associated digital imaging correlation. Philadelphia: Lippincott-Raven, 1997.

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Cavadini, Marco. Concept and model of a multiprocessor system for high resolution image correlation. Konstanz: Hartung-Gorre, 1999.

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R, Matthys Donald, and United States. National Aeronautics and Space Administration., eds. Characterization of fluid flow by digital correlation of scattered light. Huntsville, AL: Univ. of Alabama in Huntsville, 1989.

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Lin, Ming-Tzer, Cesar Sciammarella, Horacio D. Espinosa, Cosme Furlong, Luciano Lamberti, Phillip Reu, Michael Sutton, and Chi-Hung Hwang, eds. Advancements in Optical Methods & Digital Image Correlation in Experimental Mechanics, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30009-8.

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Lamberti, Luciano, Ming-Tzer Lin, Cosme Furlong, Cesar Sciammarella, Phillip L. Reu, and Michael A. Sutton, eds. Advancement of Optical Methods & Digital Image Correlation in Experimental Mechanics, Volume 3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97481-1.

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Lin, Ming-Tzer, Cosme Furlong, Chi-Hung Hwang, Mohammad Naraghi, and Frank DelRio, eds. Advancements in Optical Methods, Digital Image Correlation & Micro-and Nanomechanics, Volume 4. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17471-1.

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Center, Langley Research, ed. Digital PIV (DPIV) software analysis system: Under contract NAS1-19505. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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United States. National Aeronautics and Space Administration., ed. Fuzzy interference enhanced information recovery from digital PIV using cross-correlation combined with particle tracking. [Washington, D.C.]: National Aeronautics and Space Administration, 1995.

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Book chapters on the topic "Digital image correlation"

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Iskander, Magued. "Digital Image Correlation." In Modelling with Transparent Soils, 137–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02501-3_8.

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Bornert, Michel, François Hild, Jean-José Orteu, and Stéphane Roux. "Digital Image Correlation." In Full-Field Measurements and Identification in Solid Mechanics, 157–90. Hoboken, NJ USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118578469.ch6.

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Sause, Markus G. R. "Digital Image Correlation." In In Situ Monitoring of Fiber-Reinforced Composites, 57–129. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30954-5_3.

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Michael A., Michael A., Jean-José Orteu, and Hubert W. Schreier. "Digital Image Correlation (DIC)." In Image Correlation for Shape, Motion and Deformation Measurements, 1–37. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-78747-3_5.

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Gdoutos, Emmanuel E. "Digital Image Correlation (DIC)." In Solid Mechanics and Its Applications, 251–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89466-5_12.

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Cristofolini, Luca. "Overview of Digital Image Correlation." In Springer Series in Solid and Structural Mechanics, 187–213. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06086-6_5.

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Michael A., Michael A., Jean-José Orteu, and Hubert W. Schreier. "Volumetric Digital Image Correlation (VDIC)." In Image Correlation for Shape, Motion and Deformation Measurements, 1–16. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-78747-3_8.

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Witz, J. F., P. Lecomte-Grosbras, A. Morch, C. Martel, F. Lesaffre, and M. Brieu. "Digital Image Correlation for Large Strain." In International Digital Imaging Correlation Society, 163–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51439-0_39.

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Barrière, L., O. Cherrier, J. C. Passieux, M. Bouquet, and J. F. Ferrero. "3D Digital Image Correlation Applied to Birdstrike Tests." In International Digital Imaging Correlation Society, 17–20. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51439-0_4.

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Jaminion, S., N. W. Nelson, J. P. Chambard, N. Swiergel, and F. Hild. "CorreliSTC: A Global Approach in Digital Image Correlation." In International Digital Imaging Correlation Society, 277–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51439-0_66.

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Conference papers on the topic "Digital image correlation"

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Chumakov, Alexandr G., Alexandr V. Kisil, Andry V. Kovalenko, Vitalij N. Kurashov, N. G. Nakhodkin, and Dmitrij V. Podanchuk. "Optoelectronic system of digital holographic image processing." In Holography, Correlation Optics, and Recording Materials, edited by Oleg V. Angelsky. SPIE, 1993. http://dx.doi.org/10.1117/12.165403.

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Siebert, Th, Th Becker, and K. Splitthof. "High Speed Digital Image Correlation Techniques." In SAE 2006 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2006. http://dx.doi.org/10.4271/2006-01-0528.

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Milosevic, M., N. Mitrovic, and A. Sedmak. "Digital image correlation analysis of biomaterials." In 2011 15th IEEE International Conference on Intelligent Engineering Systems (INES). IEEE, 2011. http://dx.doi.org/10.1109/ines.2011.5954784.

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Felipe-Sesé, Luis, Francisca Andrés-Castro, Ángel Molina-Viedma, Elías López-Alba, and Francisco Díaz-Garrido. "Digital Image Correlation Employing Thermal Marking." In ICEM 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/psf2022004003.

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Tehrani, Amin Darabnoush, Zahra Kohankar Kouchesfehani, and Mohammad Najafi. "Pipe Profiling Using Digital Image Correlation." In Pipelines 2020. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483206.005.

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Rohe, Daniel, Bryan Witt, and Phillip Reu. "Modal Testing using Digital Image Correlation." In Proposed for presentation at the iDICs 2022 held November 7-10, 2022 in Boston, MA. US DOE, 2022. http://dx.doi.org/10.2172/2005957.

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Montgomery, Veronica, Oscar Martinez, Paul Nogradi, Lance Lowe, and Abiodun Adeniyi. "Free Drop Impact Data Acquisition Using Digital Image Correlation." In ASME 2023 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/pvp2023-106649.

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Abstract Radioactive materials are widespread throughout our everyday activities and are used in many different types of industries. There are about 3 million shipments of radioactive materials that occur each year in the United States. Because radioactive materials are so widespread, safety and security are top priorities for the Package Testing Program (PTP) at Oak Ridge National Laboratory (ORNL), and effective and accurate data collection methods are imperative. The packaging used to carry nuclear materials is subjected to rigorous tests to verify its durability. One such test is the free-drop test. Impact data are often collected through an accelerometer attached to the test unit. Unfortunately, these devices are easily broken in the testing environment and are expensive to procure, install, and post process. Digital image correlation (DIC) using high-speed images offers a non-contact alternative. DIC is an optical method that employs image registration techniques to accurately measure changes in images. To verify the suitability of the cameras and process, a test weight was dropped from various heights, and the impact acceleration was derived using DIC. This paper compares the results with data from the attached accelerometer. A total of nine drop tests were performed with three different image capture rates (frames per second) and from three different package drop heights. An ANSYS LS-Dyna model was also used to determine theoretically predicted deceleration rates for comparison with the DIC and accelerometer data.
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Jing, Xu, Yang Xiao Hong, Shao Xiang Xin, and Meng Xian Ying. "Medical Image Mosaic Technology Based on Image Phase Correlation." In 2012 4th International Conference on Digital Home (ICDH). IEEE, 2012. http://dx.doi.org/10.1109/icdh.2012.51.

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Abeywickrema, Ujitha A., Rudra Gnawali, and Partha P. Banerjee. "Identification of 3D objects using correlation of holograms." In Applications of Digital Image Processing XLI, edited by Andrew G. Tescher. SPIE, 2018. http://dx.doi.org/10.1117/12.2322668.

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Song, Woo-Jin, and William A. Pearlman. "A Minimum-Error, Minimum-Correlation Filter For Images." In Applications if Digital Image Processing IX, edited by Andrew G. Tescher. SPIE, 1986. http://dx.doi.org/10.1117/12.976223.

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Reports on the topic "Digital image correlation"

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Palaviccini, Miguel, Daniel Z. Turner, and Michael Herzberg. Digital Image Correlation for Performance Monitoring. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1238316.

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Turner, Daniel Z., Richard B. Lehoucq, and Carlos A. Garavito-Garzon. PDE Constrained Optimization for Digital Image Correlation. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1494349.

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Bigger, Rory, Benoît Blaysat, Christofer Boo, Manuel Grewer, Jun Hu, Amanda Jones, Markus Klein, et al. A Good Practices Guide for Digital Image Correlation. Edited by Elizabeth Jones and Mark Iadicola. International Digital Image Correlation Society, October 2018. http://dx.doi.org/10.32720/idics/gpg.ed1.

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Bigger, Rory, Benoît Blaysat, Christofer Boo, Manuel Grewer, Jun Hu, Amanda Jones, Markus Klein, et al. A Good Practices Guide for Digital Image Correlation. Edited by Elizabeth Jones and Mark Iadicola. International Digital Image Correlation Society, October 2018. http://dx.doi.org/10.32720/idics/gpg.ed1/print.format.

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Hall, Leslie. Digital Image Correlation of Flapping Wings for Micro-Technologies. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada558423.

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Casias, Zachary. High Throughput Coefficient Thermal Expansion Testing Utilizing Digital Image Correlation. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1898723.

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Mahadevan, Sankaran, Vivek Agarwal, Binh T. Pham, and Neal Kyle. Digital Image Correlation of Concrete Slab at University of Tennessee, Knoxville. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1364495.

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Kane, Kenneth, Samuel Bell, Ben Garrison, Brandon Johnston, Nathan Capps, and Kory Linton. Report Summarizing Progress in Digital Image Correlation Analysis of Burst Phenomenon. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1865738.

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Granzow, Brian N., and Daniel Thomas Seidl. Adjoint-based Calibration of Plasticity Model Parameters from Digital Image Correlation Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1474264.

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Barker, Craig, Douglas Howle, Terry Holdren, Jeffrey Koch, and Raquel Ciappi. Results and Analysis from Mine Impulse Experiments Using Stereo-Digital Image Correlation. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada561954.

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