Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology |
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| Institution: | 1. Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Korea Atomic Energy Research Institute, 989-111 Daedeok-daero, Yuseong-gu, Daejeon 305-353, Republic of Korea;1. Department of Engineering Mechanics, Shanghai Jiao Tong University, Shanghai 200240, China;2. School of Computer Science, Fudan University, Shanghai 201203, China;1. Rolls-Royce, Pride Park, Derby DE24 8LY, UK;2. Department of Mechanical Engineering, Politecnico di Milano, via La Masa 1, 20156 Milan, Italy;3. Department of Civil and Environmental Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA;4. Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA;1. Rolls-Royce plc, MV-GF, Victory Road, P.O. Box 31, Derby DE24 8BJ, UK;2. Dept. of Civil and Environmental Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA;3. Dept. of Mechanical Engineering, Politecnico di Milano, via La Masa 1, 20156 Milan, Italy;4. Dept. of Mechanical Engineering, University of South Carolina, 300 Main Street, 29208 Columbia, SC, USA |
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| Abstract: | In digital image correlation (DIC), speckle patterns are generated on the surface of a specimen to resolve uniqueness issues. Thus, speckle patterns significantly affect the accuracy of image correlation. To assess the quality of speckle patterns, the standard deviation of gray intensities within each speckle (SDGIS) is introduced as a new metric. On the basis of the cumulative distribution of SDGIS, speckle-pattern quality measurement (ρ) is proposed, which integrates the features of gray intensity and speckle morphology. Twelve speckle patterns are generated by changing the spraying time and nozzle sizes of an airbrush because these are associated with the speckle volume fraction and speckle size, respectively. In addition, three displacement fields are used to investigate the effects of speckle patterns on the accuracy of the DIC results. For the 12 speckle images associated with the three displacement fields, the correlation results demonstrate that the proposed speckle-pattern quality measurement is inversely proportional to the averaged error of the subset method. This is statistically confirmed by evaluating the correlation coefficient and p-value. Furthermore, the error of the subset method is more affected by speckle patterns than the subset size when the subset size is sufficiently large. |
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| Keywords: | Speckle-pattern quality Digital image correlation Deformation measurement Gray intensity Speckle morphology |
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