## 3 Results
### The New World Order  
In this experiment, we evaluated the edge detection algorithm on a highly textured acrylic-on-canvas painting. This artwork is characterized by dense material layering and brushwork, resulting in a richly varied topography. Unlike artwork with clearly defined boundaries, this composition presents edges that emerge through physical texture and subtle tonal shifts. Figure 7 presents the error maps generated by each edge detection algorithm, offering a visual comparison of their performance in terms of misclassifications. The Canny method shows a substantial amount of tolerated true positives (green), indicating effective edge detection within the tolerance region. However, false negatives (purple) are visible, particularly around finer structural areas, such as the inner contours of objects. False positives (orange) are present, suggesting moderate noise sensitivity and issues with its capability to detect edges whenever there is a different texture or change. The Sobel method exhibits a similar performance to Canny, showing details such as the jacket's pattern or the feathers that are not considered edges on the ground truth. This results in small details being shown as false positives. The Laplacian method detects a high number of true positives in the whole artwork. However, on a few occasions, like the interior of the window or the olive branch that the bird is holding, some edges are not detected, generating some false negatives. The number of false positives increases considerably, in contrast to the other methods. DexiNed produces false positives around the true edges of the images while missing some of the real edges. Table 6 provides complementary details for each experiment, including the parameters used and the quantitative metrics obtained, allowing for a more comprehensive evaluation of each method. The Sobel method achieves the highest F1 score (0.4190) and FOM (0.2681), indicating a strong balance between detecting true edges and minimizing false detections. It also has \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Preprocess** & **Kernel Size** & **Sigma** & **F1 Score** & **SSIM** & **FOM** & **Runtime (s)** \\ \hline Sobel & Equalized & 5 & & 0.4190 & 0.8249 & 0.2681 & 24.9849 \\ Canny & Equalized & & 0.5 & 0.4127 & 0.8240 & 0.2519 & 23.7141 \\ Laplacian & Gray & 5 & & 0.4091 & 0.6515 & 0.1761 & 23.7148 \\ DexiNed & Gray & & & 0.2534 & 0.8243 & 0.1383 & 51.9780 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics and parameters for each experiment on the artwork “The new world order”. Figure 7: Error maps of each algorithm for the artwork “The new world order”. (**a**) Canny; (**b**) Sobel; (**c**) Laplacian; (**d**) DexiNed. the highest SSIM value (0.8249), suggesting that the detected edges closely resemble the structural layout of the ground truth. This performance highlights Sobel's effectiveness when combined with histogram equalization preprocessing and a 5 \(\times\) 5 kernel. The Canny method, also using an equalized image, follows closely, with an F1 score of 0.4127 and an SSIM of 0.8240. Although slightly lower than Sobel, its performance remains competitive. However, its FOM (0.2519) was marginally lower, indicating a slightly reduced spatial precision in edge localization. The Laplacian method, applied to a grayscale image with a 5 \(\times\) 5 kernel, shows a moderate drop in performance. Its F1 score (0.4091) was comparable to Canny and Sobel, but its SSIM value (0.6515) was significantly lower, reflecting a weaker structural match with the ground truth. The FOM (0.1761) also indicated reduced spatial accuracy, likely due to increased sensitivity to noise and less effective edge continuity. Surprisingly, DexiNed, despite its superior visual performance, recorded the lowest scores in some metrics, namely, F1 score (0.2534) and FOM (0.1383).