## 3 Results and Discussion
### Minimising the Effect of Contrast Lack-Of-Uniformity: Proposed Approaches  
Contrast characteristics showed a high influence upon contour reconstruction accuracy. Lack of uniformity in contrast between the test target and its surrounding background was directly related to poor results. This lack of uniformity was related to the directional illumination in the FBS, causing an anisotropic distribution of bright-ened and shadowed areas in the background. In this test design, worsening of results caused by contrast anisotropy was directly related to the distance between the target layer and the background (\(H\)). Accordingly, if \(H\) was large enough (the case of \(S02\)), FBS illumination did not reach the background with enough intensity to generate highly anisotropic brightness and shadow patterns. In this situation, background ill-mination was more uniform, and this caused a smoother appearance. The explanation of this behaviour is related to tested edge-detection algorithms because they use a threshold (\(T\)) value to promote image points to contour points. This approach is clearly not adequate when the intensity gradients present non-uniform characteristics, because of contrast anisotropy. This phenomenon can be affected by the presence of abnormal features, such as seams or oozing, causing local contour reconstruction discontinuities or locally reducing its accuracy. Moreover, results could be even worse if the vertical distance between the contour and the background is not constant. Accordingly, tested edge-detection algorithms should only be used for AM \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Coverage Priority**} & \multicolumn{4}{c}{**Accuracy Priority**} \\ \cline{2-10}  & \(T\) & \(S_{f}\) & \(\begin{array}{c}C_{f}\\ \{\%\}\end{array}\) & \(\begin{array}{c}\overline{D}\\ \{\%\}\end{array}\) & \(\begin{array}{c}\sigma_{D}\\ \{\mu\}\end{array}\) & \(T\) & \(S_{f}\) & \(\begin{array}{c}C_{f}\\ \{\%\}\end{array}\) & \(\begin{array}{c}\overline{D}\\ \{\%\}\end{array}\) & \(\begin{array}{c}\sigma_{D}\\ \{\mu\}\end{array}\) \\ \hline
**Sobel** & 0 & 1 & 100 & 101.9 & 60.66 & 5 & 1 & 83.3 & 56.59 & 57.30 \\
**Prewitt** & 0 & 1 & 100 & 100.6 & 60.51 & 5 & 1 & 81.1 & 57.17 & 57.59 \\
**Roberts** & 0 & 1 & 100 & 100.3 & 59.84 & 5 & 1 & 98.8 & 63.11 & 58.65 \\
**Zerocross** & 0 & 39 & 100 & 98.7 & 60.94 & 0 & 39 & 100.0 & 98.66 & 60.94 \\
**Canny** & 10 & 1 & 100 & 54.9 & 48.97 & 20 & 1 & 77.5 & 31.93 & 17.75 \\
**ApproxCanny** & 10 & 1 & 100 & 73.7 & 49.37 & 5 & 55 & 76.6 & 55.03 & 25.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimal combinations of test parameters for S04. contour characterisation in ar-rangements with uniform contrast between the background and layer. This also means that a robust FBS-based OMM verification procedure of AM layers must include ele-ments that minimise the influence of contrast anisotropy. We propose two possible approaches to deal with this problem in the future: a uniform contrast approach and adaptive edge-detection. * A uniform contrast approach would consist of a combination of illumination arrangements and image processing, tailored to assure that each contour segment presented similar contrast characteristics. A possible arrangement would imply consecutive scans of the contour using different relative orientations between part and light direction, and merging those specific areas where contrast characteristics were similar. This approach would affect the physical system, because an additional degree of freedom between part and sensor should be considered. Additionally, it also complicates image processing, because it would be necessary to relate all scans to the same reference system, to cluster normal-to-contour orientations, to remove areas with inappropriate contrast characteristics, and to merge remaining areas into a single image. Finally, it is not clear that this approach could provide an adequate solution to the problem of local contour abnormalities.