Speckle pattern quality assessment for digital image correlation
Speckle pattern quality assessment for digital image correlation
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DOI：
10.1016/j.optlaseng.2013.03.014
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发表时间：
2013-12-01
影响因子：
4.6
通讯作者：
Dulieu-Barton, J. M.
中科院分区：
文献类型：
--
作者：
Crammond, G.;Boyd, S. W.;Dulieu-Barton, J. M.
为了执行数字图像相关（DIC），每个图像被分成称为子集或询问单元的像素组。较大的询问单元允许更高的应变精度，但降低了数据场的空间分辨率。因此，DIC的空间分辨率和测量精度受到图像分辨率的限制。本文给出了图形中散斑的大小与密度的关系，指出图形的物理性质对测量精度有很大的影响。这些物理性质往往被模式评估标准所忽视，这些标准关注的是全局图像信息内容。为了解决这个问题，设计了一种使用边缘检测的鲁棒形态学方法来评估图像分辨率从23到705像素/mm的不同散斑模式的物理特性。根据模拟变形评估从模式属性分析预测的趋势，确定应用方法的微小变化如何导致测量精度的巨大变化。该方法的一个例子包括，以证明从分析中得出的模式属性可以用来指示模式质量，从而最大限度地减少DIC测量误差。描述了进行的实验，以验证形态学评估和误差分析的结果。(C) 2013 Elsevier Ltd.版权所有。
To perform digital image correlation (DIC), each image is divided into groups of pixels known as subsets or interrogation cells. Larger interrogation cells allow greater strain precision but reduce the spatial resolution of the data field. As such the spatial resolution and measurement precision of DIC are limited by the resolution of the image. In the paper the relationship between the size and density of speckles within a pattern is presented, identifying that the physical properties of a pattern have a large influence on the measurement precision which can be obtained. These physical properties are often overlooked by pattern assessment criteria which focus on the global image information content. To address this, a robust morphological methodology using edge detection is devised to evaluate the physical properties of different speckle patterns with image resolutions from 23 to 705 pixels/mm. Trends predicted from the pattern property analysis are assessed against simulated deformations identifying how small changes to the application method can result in large changes in measurement precision. An example of the methodology is included to demonstrate that the pattern properties derived from the analysis can be used to indicate pattern quality and hence minimise DIC measurement errors. Experiments are described that were conducted to validate the findings of morphological assessment and the error analysis. (C) 2013 Elsevier Ltd. All rights reserved.