92 PL. Reu Table 14.2 Strain variance error using the optimized interpolant Filter Aliased Speckle Resolved Speckle Mixed Speckle None 100 49 33 2Lambda 333 56 85 4Lambda 899 41 43 For the fully resolved speckles, the anti-aliasing filter improves the results for the bi-linear interpolant, and has no influence with the optimized interpolant. This is most likely because there is no loss of contrast, because the speckles are fully resolved; however, the edges of the speckles, which in the printed image have hard edges, are filtered and softened. This improves the ability of the interpolant to be able to fit the contrast gradients. Table 14.2 presents the tabulated results of the strain variance in the three speckle regions for the three cameras. There were no noticeable bias errors for the optimized interpolant. For the mixed speckle patterns, with both aliased and fully-resolved speckles, anti-aliasing does seem to improve the results for the linear interpolants. This is because removing the aliased information helps, while there is less loss of contrast due to the presence of the fully-resolved speckles. For the optimized interpolant, there is no improvement from the antialiasing filters. 14.4 Conclusions It has been known that aliased speckles, and speckle size in general, has an influence in the quality of DIC results. This short study confirms those results. Aliased speckles, for either an optimized or bi-linear interpolant (and presumably all interpolants) negatively impact the DIC results. For bi-linear interpolants, where the bias errors are larger, the results are clearly seen in the greater peak-to-valley errors for the aliased patterns regardless of whether there is an antialiasing filter. For the optimized interpolant, the strain noise is worse for aliased speckles, again regardless of whether there is filtering. However, for the optimized interpolant, adding the antialiasing filter actually made the results much worse than having no filter when aliased speckles were present. This was true of the bi-linear as well but to a much smaller extent. This is because the loss of contrast in the image due to filtering out the small speckles is worse than the influence of the speckle aliasing. For fully-resolved speckles, filtering has a 2 improvement for the bi-linear interpolants, and a very modest improvement on the optimized interpolants. This was again approximately true for the mixed speckle pattern. These results were somewhat surprising as it was theorized that removing aliased content would greatly improve the results. This is only true when a “bad” interpolant is used for the analysis. When a good interpolant is used, the gains are relatively modest. This is again because the loss of contrast has a larger influence on the results than the added noise of the aliased information. This study does confirm that having an aliased speckle is much worse (approximately 3 ) than having a larger and fully resolved speckle. Therefore, aliased speckles should always be avoided. Finally, with modern DIC software, it seems that adding an anti-aliasing filter to commercial machine vision cameras is not worth the added cost and effort. Acknowledgements I would like to thank my summer intern, Hailey Stock for acquiring this data, and Paul Farias for installing the anti-aliasing filters on the cameras. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract No. DE-AC04-94AL85000. References 1. Sutton, D.A., Orteu, J.J., Schreier, H.W.: Image Correlation for Shape, Motion and Deformation Measurements. Springer, New York (2009) 2. Schreier, H.W., Braasch, J.R., Sutton, M.A.: Systematic errors in digital image correlation caused by intensity interpolation. Opt. Eng. 39(11), 2915–2921 (2000)
RkJQdWJsaXNoZXIy MTMzNzEzMQ==