Dec. 2019

Article Contents

Chinese Optics> 2019> 12(6): 1329-1337.

HU Hui-ran, DAN Xi-zuo, ZHAO Qi-han, SUN Fang-yuan, WANG Yong-hong. Automatic extraction of speckle area in digital image correlation[J]. Chinese Optics, 2019, 12(6): 1329-1337. doi: 10.3788/CO.20191206.1329

Citation:

HU Hui-ran, DAN Xi-zuo, ZHAO Qi-han, SUN Fang-yuan, WANG Yong-hong. Automatic extraction of speckle area in digital image correlation[J].Chinese Optics, 2019, 12(6): 1329-1337.doi:10.3788/CO.20191206.1329

Citation:

HU Hui-ran, DAN Xi-zuo, ZHAO Qi-han, SUN Fang-yuan, WANG Yong-hong. Automatic extraction of speckle area in digital image correlation[J].Chinese Optics, 2019, 12(6): 1329-1337.doi:10.3788/CO.20191206.1329

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Automatic extraction of speckle area in digital image correlation

doi:10.3788/CO.20191206.1329

School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, China

Funds:

National Key Research and Development Program of China2016YFF0101803

National Natural Science Foundation of China51805137

More Information

Corresponding author:WANG Yong-hong, E-mail:yhwang@hfut.edu.cn
Received Date:12 Dec 2018
Rev Recd Date:01 Feb 2019
Publish Date:01 Dec 2019

Abstract

Abstract

In digital image correlation measurements, the speckle area is manually selected before the correlation calculation is performed to define the matching area. With the development of industrial automation, facing the complex shape of the speckle area and the need to measure a large number of speckle images, it is crucial to find an automatic area extraction method. According to the characteristics of speckles and by comparing various conventional edge detection methods, a decision function based on second-order gradient entropy is proposed for automatically detecting speckle regions in images. By analyzing different speckle images, the optimal sub-region entropy size interval and the adaptive threshold interval in different speckle patterns were determined and the automatic extraction of the speckle region were completed by using connected region segmentation. The method was verified by using the actual speckle pattern. The experimental results show that when the entropy size of the subregion is more than 10 pixel, the decision function is sensitive to the speckle area. When the adaptive threshold value is within the range of Q-1.25 to Q, the speckle area and the background area can be effectively separated. The automatic extraction of a speckle area can be completed and the selection of speckle area before correlation calculation is achieved.
- digital image correlation,
- speckle,
- second-order gradient entropy,
- adaptive threshold

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References (17)

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