## 3 Results
### Zoosofias No. 100  
In this experiment, we evaluated the edge detection algorithm on a natural scene that features two animals in a forest.  
Figure 4 presents the error maps generated by each edge detection algorithm, offering a visual comparison of their performance in terms of misclassification.  
One of the first noticeable aspects is that all classical, non-deep learning algorithms are influenced by the physical characteristics of the artwork, such as the texture of its materials, which affect the edge detection performance.  
The Canny method produces some clean edges but introduces several false positives (orange areas), particularly in the background and textured regions. It also misses some details, resulting in false negatives (purple lines) among the animals, especially of their limbs, hair, and ears.  
The Sobel method produces thicker and fragmented edges, with an increase in false positives across the sky and ground. It also fails to capture some fine anatomical features, which leads to false negatives in some regions.  
The Laplacian method exhibits the highest density of false positives, generating excessive responses in areas with subtle tonal transitions. Although it detects some structural contours, it lacks precision and introduces significant noise. False negatives are also  
Figure 4: Error maps of each algorithm for the “Zoosofias no. 100” artwork. (**a**) Canny; (**b**) Sobel; (**c**) Laplacian; (**d**) DexiNed.  
present, particularly in the internal contours of the animals, indicating limited effectiveness at capturing fine details.  
DexiNed, a deep learning-based method, offers the most coherent and continuous edge structures. It achieves a higher number of tolerated true positives, especially along the main outlines of the animals. However, it still presents false positives in terms of background textures and false negatives in low-contrast or thin-edged regions, such as the legs and ears.  
Table 3 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 Canny method was applied to an equalized image with a sigma of 0.3, meaning that the lower and upper thresholds were calculated based on that parameter and the median of the image. It achieves the highest F1 score (0.4000), indicating a good balance between precision and recall. It also maintains a high SSIM score (0.8833), suggesting strong structural similarity with the ground edges, and the best FOM (0.2075), reflecting accurate edge detection.  
Sobel, when also applied to an equalized image, shows slightly lower performance across all metrics, with an F1 score of 0.3668 and a noticeable drop in SSIM (0.8143), indicating less structural similarity. The Laplacian algorithm, using a grayscale input and a kernel size of 5, results in slightly lower values for all metrics, which suggests an error in the edge detection, as mentioned in the visual comparison.  
DexiNed, despite being a deep learning-based method, achieves the highest SSIM score (0.8963). However, its F1 score (0.2770) is the lowest of all methods, indicating that while its output is visually coherent, there might be more errors compared to the other methods.  
In terms of runtime, classical algorithms demonstrated significantly faster execution compared to the deep learning-based DexiNed. Canny completed its processing in 0.808 s, while Sobel and Laplacian required 1.457 and 0.861 s, respectively. In contrast, DexiNed took 39.95 s to process the same image. This substantial difference highlights the computational efficiency of traditional methods, which may be advantageous in time-sensitive conservation workflows or when operating with limited hardware resources.