## 3 Results
### Investigation of the Influence of Calibration Parameters on the Results of Strinin/Stress Analysis
In our case, a local regression filter with the specified level described in publications [24; 41; 48] was used to smooth contour and displacement fields. In the first phase, two variables automatically provided by the Istra4D V4.3.0 software were used as parameters informing about the influence of the calibration parameters on the measurement results. These are the estimated contour statistical error radius (Figure 9) and the deviation of the specimen contour from the best-fit plane (BFP, Figure 10). By analysing the results shown in Figure 9, it was found that the estimated radius of statistical error does not differ significantly when using the high-precision calibration targets supplied by the manufacturer (1.5 mm and 3.0 mm), mainly from the ninth (and each higher) registered position of the calibration target. When using a calibration target manufactured with a lower manufacturing precision (2.0 mm), the estimated error is higher for five up to nine recorded positions and becomes stable from approximately the 12 th registered position of the target. It is also interesting to note that the values of the estimated error obtained in this way became slightly smaller after the 11 th registered position of the target than those of the original calibration targets. The plots showing the estimated statistical error are of the same character for each loading level. There are no significant changes in their values, which are of the 10\({}^{-1}\) um order. Based on the analysis of the plots of the deviation of the specimen surface from the best-fit plane (Figure 10), it can be concluded that the plots are also of the same character for each loading level and differ only minimally in values. In the case of using the largest target (3.0 mm), it is possible to observe an approximately constant level for any number of registered positions of the calibration target. Moreover, for this target, the lowest variance of BFP deviation values is observed, and from about the 12 th registered position of the target, the values obtained show only slight differences. On the contrary, the most significant variance in values is achieved for a target size of 1.5 mm, which occurs for the number of registered positions of the calibration target less than 12. When using the target with the lowest manufacturing precision (2.0 mm), the variance of the values is not so significant. From its 12 th registered position, the BFP deviation reaches a similar level as the other targets. Figure 8: Captured reference images with marked evaluation area. Figure 10: Plots of the specimen surface deviation from the best-fit plane: (**a**) \(F\) = 400 N, (**b**) \(F\) = 800 N, (**c**) \(F\) = 1200 N, (**d**) \(F\) = 1600 N.  
Figure 9: Plots of the estimated contour statistical error radius: (**a**) \(F\) = 400 N, (**b**) \(F\) = 800 N, (**c**) \(F\) = 1200 N, (**d**) \(F\) = 1600 N.  
The causes of the results obtained can be explained from the plots of the cameras' intrinsic and extrinsic parameters, or lens distortions and their errors, estimated by the Q-400 Dantec Dynamics correlation system automatically during calibration (see Appendix A). Almost all of the plots of the parameters mentioned above obtained using the largest target (3.0 mm) reach a constant level and, simultaneously, the lowest estimated error. When analysing the camera parameters obtained with the smallest target (1.5 mm), the most significant variance of parameters values for the first 12 registered positions can be observed, especially in the case of determining the position of the principal point or the lens distortion coefficients, which harmed the estimation of the deviation from the best-fit plane when evaluated using the calibration files from the first half of the calibration process. The authors attribute this phenomenon to the target size, which covers only approximately 12.5% of the cameras' field of view used in calibration mode. Thus the correlation system needs to acquire and evaluate a larger number of its different positions to determine these parameters correctly.