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Digital volume correlation (DVC) is a powerful technique to measure the deformation inside the material or a structure. In the DVC working steps, the displacements of points of interest (POIs) are first calculated by tracking the correspondences between the volumes acquired before and after deformation using speckle correlation, which is then followed by strain calculation using the derivative of these displacements. Generally, displacement accuracy of the POIs heavily depends on the speckle quality. Unlike the speckles on the surface of test object for digital image correlation analysis can be painted or fabricated, however, the speckles inside the material or a structure for DVC analysis are often not controllable. In addition, the volume acquired by the common volumetric imaging equipment generally has limited resolution and large image noise. As a result, the poor speckle quality and large image noise introduce great displacement outliers. Strain calculation is vulnerable to displacement noise because of the high sensitivity of differential calculation to noise. Therefore, it is critical to discard the displacement outliers to guarantee the strain calculation accuracy. In this work, I proposed a ticket voting method to detect displacement outliers and then exclude them in the strain calculation. In the ticket voting method, each POI is voted by its neighboring POIs based on the residual errors in strain calculation. If this POI obtains more than half of the total tickets, it will be included in strain calculation, otherwise excluded. Both simulation and experiment results showed that the proposed method had significantly higher accuracy than the conventional method; and our DVC method can avoid the miscalculated strain concentration.
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