14 Using Anti-aliasing Camera Filters for DIC: Does It Make a Difference? 91 14.3 Results The images for the three cameras were analyzed using standard subset based commercial DIC codes. The subset size was 49 pixels, the step size was 10 pixels, the strain window was 15 data points yielding a virtual strain gage size of 189 pixels. An affine shape function was used with two different interpolation functions, a bi-linear interpolant and an optimized interpolant. The bi-linear interpolant was chosen because of its poor performance with respect to aliased images. The optimized interpolant was used to show how a more typical (and better) interpolant interacted with the speckle patterns and the three camera images. Figure 14.3 shows the strain bias errors for the linear interpolant for all three cameras and all three speckle patterns as labeled in the image. The scales are identical showing the relative bias errors between the different patterns and the effect of the anti-aliasing filtering on the results. The tabulated results for the linear interpolant are shown in Table 14.1 and are the peak-to-valley bias error from a line cut in each region of the image. The aliased speckle pattern is clearly the worst for linear interpolants for all three cameras. However, the anti-aliasing filter does not improve the results as may be expected. This is because the loss of contrast from the filtering of the under-resolved speckles has a more dramatic influence on the results than the deleterious influence of the aliased speckle information. Fig. 14.3 Strain results ("yy) for all three cameras and all three speckle patterns with a bi-linear interpolant. Scales are identical showing the relative magnitude of the interpolation bias error in the strain field Table 14.1 Strain bias errors (in microstrain) caused by the aliased images using a bi-linear interpolant. Peak-to-Valley strain error is reported Filter Aliased Speckle Resolved Speckle Mixed Speckle None 4520 1309 1388 2Lambda 5393 1182 808 4Lambda 7437 590 589
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