## 3 Results and Discussion
### Analysis of Contour Reconstruction Reliability for S03  
Quality indicators worsened for S03, independent of which edge-detection method had been applied. Coverage results dropped with respect to the previous scenarios, so that only Approxcanny or Canny methods provided a high range of \(T\) and \(S_{f}\) combinations that achieved 100% C\({}_{f}\). Worsening of contour characterisation quality was also observed. In fact, results were so poor that the 5-10 \(\upmu\)m colour scale used for the first two specimens had to be substituted by a 10-100 \(\upmu\)m scale (Figure 14). Figure 13: Polar \(R_{d}\) graphs for S02 using a \(T15/S_{f}15\) test combination: without annular cropping (**left**) and with annular cropping (**right**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(T\) & \(S_{f}\) & \(C_{f}\)(\%) & \(\overline{D}\) & \(\sigma_{D}\) (\(\upmu\)m) \\ \hline
**Without Annular Cropping** & 15 & 15 & 100 & 154.8 & 208.4 \\
**With Annular Cropping** & 15 & 15 & 100 & 12.41 & 18.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MQI results for S02 using a \(T15/S_{f}15\) test combination without annular cropping and with annular cropping. Figure 14: MQI results as a function of \(S_{f}\) and \(T\) for every \(M\) calculated for SO3. Observed variations in \(\overline{D}\) and \(\sigma_{D}\) were not as sharp as those observed in \(C_{f}\), but their response to \(S_{f}\) or \(T\) variations was quite similar: high \(T\) values worsened \(\overline{D}\) and \(\sigma_{D}\) results, whereas \(S_{f}\) did not to have a significant influence. Reduction in high-quality combinations influenced the differences observed between coverage priority and accuracy priority. In fact, \(C_{f}\) results were quite similar under both criteria, apart from Canny and ZeroCross. In fact, ZeroCross showed a noticeable reduction in \(C_{f}\) (100% under coverage priority against 9.72% under accuracy priority). Roberts would be the recommendation here for an optimal scan under the coverage priority criterion, whereas Canny would be selected under the accuracy priority criterion (Table 6). Nevertheless, the \(\sigma_{D}\) minimum value was 25.81 \(\upmu\)m (Canny; \(T=20\); \(S_{f}=15\)), which is nearly four times higher than the best result obtained for \(S02\). Similarly, the \(\overline{D}\) minimum value was 42.06 \(\upmu\)m for the same configuration, which is more than five times higher than the optimal for \(S02\). These results indicate that using an optimal processing configuration was critical, because there were fewer adequate combinations. Moreover, even when optimal combinations were applied, results were several times worse than those observed in sharper and more uniform contrast scenarios. To discuss possible causes of this overall worsening, Figure 15 contains samples of contour points obtained with Canny for three different test combinations: \(T01/S_{f}15\), \(T20/S_{f}15\) and \(T60/S_{f}15\). The optimal combination under the accuracy priority criterion (\(T20/S_{f}15\)) provided clear contour lines for 90\({}^{\circ}\) and 180\({}^{\circ}\) orientations, but failed to remove multiple-contour detection issues for 0\({}^{\circ}\) and 45\({}^{\circ}\) orientations (Figure 16). Therefore, although providing a reasonably good coverage, accuracy indicators were noticeably worse than those achieved for 502 (Tables 4 and 6). Lower thresholds severely increased the multiple-contour detection because the algorithm registered many weak gradients as contour candidates. This circumstance allowed for achieving a full coverage but worsened other indicators. Increased threshold reduced the number of multiple-contour issues, although it did not provide a complete suppression as can be observed for the 0\({}^{\circ}\) orientation in Figure 15. Nevertheless, it also removes large sectors (180\({}^{\circ}\)) that were accurately reconstructed at lower threshold.