The present article shares with the scientific community several image sets used for experimentally validating the noiseinduced bias on different Digital Image Correlation (DIC) formulations, as reported in Baldi et al. [1]. These sets are provided with a description of the experimental setup used for image acquisition. The basic idea is to acquire a series of images obtained by rigidly translating the target, where incremental displacement is of the order of a small fraction of a pixel. This condition requires a high-precision control of the target position through the tests. Moreover, the noise content of each image set is modulated using a statistical approach that uncouples the intensity field from the standard deviation. Lastly, the images are acquired with different exposure conditions to analyze the gray tone gradient effect on noise-induced bias.
Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation
Antonio Baldi
Conceptualization
;Pietro Maria SantucciWriting – Original Draft Preparation
;Filippo BertolinoValidation
2022-01-01
Abstract
The present article shares with the scientific community several image sets used for experimentally validating the noiseinduced bias on different Digital Image Correlation (DIC) formulations, as reported in Baldi et al. [1]. These sets are provided with a description of the experimental setup used for image acquisition. The basic idea is to acquire a series of images obtained by rigidly translating the target, where incremental displacement is of the order of a small fraction of a pixel. This condition requires a high-precision control of the target position through the tests. Moreover, the noise content of each image set is modulated using a statistical approach that uncouples the intensity field from the standard deviation. Lastly, the images are acquired with different exposure conditions to analyze the gray tone gradient effect on noise-induced bias.File | Dimensione | Formato | |
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Experimental image dataset for validation of the noise-induced bias that affects Digital Image Correlation_2022.pdf
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