# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Object Segmentation and Edge Detection on Decolorized Images
To enhance segmentation, morphological operations, such as dilation and erosion, are applied after edge detection. These operations help to refine edges by connecting disjointed segments, filling small gaps, and smoothing the boundaries. Edge detection accuracy was evaluated using Hausdorff distance [37] and mean distance that is used to evaluate edge detection accuracy by measuring the similarity between detected edges and \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
\begin{tabular}{c} 0.7614 \\ **\(\pm\) 0.122** \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: IoU for different test images and different decolorization methods. The best results are shown in bold. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Test Image** & **CDF** & **DL** & **GD** & **LAB** & **Rec. 601** & **Rec. 709** & **Rec. 2020** & \begin{tabular}{c} **Image Mean** \\ **\(\pm\) SD** \\ \end{tabular} \\ \hline gray\_surface\_fully\_saturated & **0.9947** & 0.9865 & 0.8857 & 0.7882 & 0.8514 & 0.7735 & 0.8339 & 0.8734 \(\pm\) 0.088 \\ \hline white\_surface\_fully\_saturated & **0.9951** & 0.9922 & 0.8997 & 0.7323 & 0.8529 & 0.7629 & 0.8369 & 0.8675 \(\pm\) 0.102 \\ \hline black\_surface\_fully\_saturated & 0.9885 & **0.9915** & 0.8981 & 0.9539 & 0.9180 & 0.8000 & 0.8469 & 0.9139 \(\pm\) 0.072 \\ \hline gray\_surface\_not\_saturated & **0.9947** & 0.9865 & 0.8857 & 0.7882 & 0.8514 & 0.7735 & 0.8339 & 0.8734 \(\pm\) 0.088 \\ \hline white\_surface\_not\_saturated & **1.0000** & 0.9999 & 0.9998 & 0.9999 & 0.9999 & 0.9999 & 0.9999 & 0.9999 \(\pm\) 0.000 \\ \hline black\_surface\_not\_saturated & **0.9895** & 0.9867 & 0.8978 & 0.9510 & 0.9186 & 0.8000 & 0.8564 & 0.9143 \(\pm\) 0.069 \\ \hline \multirow{2}{*}{Method mean \(\pm\) SD} & \begin{tabular}{c} **0.9548** \\ \(\pm\) 0.004 \\ \end{tabular} & \begin{tabular}{c} 0.9362 \\ **\(\pm\) 0.005** \\ \end{tabular} & \begin{tabular}{c} 0.8960 \\ **\(\pm\) 0.044** \\ \end{tabular} & \begin{tabular}{c} 0.8708 \\ **\(\pm\) 0.0112** \\ \end{tabular} & \begin{tabular}{c} 0.8874 \\ **\(\pm\) 0.059** \\ \end{tabular} & \begin{tabular}{c} 0.8216 \\ **\(\pm\) 0.090** \\ \end{tabular} & \begin{tabular}{c} 0.8604 \\ **\(\pm\) 0.065** \\ \end{tabular} & \begin{tabular}{c} 0.8604 \\ **\(\pm\) 0.065** \\ \end{tabular} & \begin{tabular}{c} 0.990 \\ **\(\pm\) 0.065** \\ \end{tabular} & \begin{tabular}{c} 0.8604 \\ **\(\pm\) 0.061** \\ \end{tabular} & \begin{tabular}{c} 0.8604 \\ **\(\pm\) 0.061** \\ \end{tabular} & \
\begin{tabular}{c} 0.8604 \\ **\(\pm\) 0.061** \\ \end{tabular} &
\begin{tabular}{c} \\ **\(\pm\) 0.06** \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: DSC for different test images and different decolorization methods. The best results are shown in bold. The analysis of the results shows that edge detection accuracy depends on the test image as well as the decolorization method. The best edge detection results were achieved for images decolorized using the GD (mean distance method mean = 0.415, Hausdorff distance method mean = 3.941) and DL (mean distance method mean = 0.616, Hausdorff distance method mean = 3.894) methods. Regarding specific images, the worst results were obtained for the test images _gray_surface_not_saturated_ (mean distance image mean = 6.480, Hausdorff distance image mean = 59.424) and _white_surface_fully_saturated_ (mean distance image mean = 5.164, Hausdorff distance image mean = 45.469), shown in Figure 8b.d. These images have the lowest contrast between objects and the background. The best results were achieved for Figure 8_white_surface_not_saturated_ (mean distance image mean = 0.660, Hausdorff distance image mean = 3.496), where all objects are clearly distinguishable from the background. This pattern is also observed in Figure 10. In Figure 10b _gray_surface_fully_saturated_ and Figure 10_white_surface_not_saturated_, edges of all objects are detected.