Graduate Thesis Or Dissertation
 

Influence of subset size, shape and orientation on texture based digital image correlation

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  • Digital Image Correlation(DIC) is one of full-field strain measurement techniques devel- oped in recent decades, which has been well-demonstrated to conduct the mechanical properties study of solid materials, by collecting data using digital cameras. X-ray to- mography(CT) imaging helps the correlation techniques expanded to three dimensional investigation of materials, known as Digital Volume Correlation(DVC). Adopting ideas from DIC, DVC is based on tracking the deformation of the material features within image volumes, by optimizing an objective function used to compare small subsets of image data from sequences of sample CT scan. However, speckling techniques generally used in DIC to create a distinguishable pattern for correlation are not applicable within an image volume of a material, so the natural texture of the materials becomes the correlation method relied on. Depending on the natural context, the DVC method cannot be adjusted in the sample images as DIC tuning speckle patterns, that natural texture does not always meet the requirements of digital correlation. This implies that we have to adjust the DVC to fit the images[1], which leads the correlation algorithm encountering some challenges when the texture appears in oriented patterns. In this thesis, a new approach is discussed to resolve the challenges by changing the subset both in target and reference images to impact the performance of the DVC and the optimization method applied, which refers to the feature of the sample texture. In order to show the influence of subset on the performance of DVC based on natural texture, these strategies of subset changes are used to generate the correlation result in the Region of Interest(ROI) with representative texture features in sample images. Different ROIs from two CT image volumes have been tested using an open source implement, which applies the objective function globally in the correlation region. By analyzing the results, a general relationship between subset adjusting and improvement of DVC performance on texture based samples is concluded. That is, a better performance of DVC can be achieved by adjusting subset to a shape similar with the feature of the natural texture in ROI.
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file details thesis_JianqingCui.pdf 2017-10-20 Public Télécharger