## 4 The M_HERACLES Toolbox
### Step 2 (1): Automatic Segmentation and Classification of Structural Supports
#### 4.2.2 Results and Analysis
However, as has been previously mentioned, its recall value is lower at 65.64%. This loss in recall value also seems to be systematic, again validating the points as argued in the previous paragraph. The overall median F1 score for the Kasepuhan dataset was 79.23%. The statistics for the St-Pierre dataset displayed a similar trend to that of the Kasepuhan; that is, it registered higher precision than a recall rate. With a median precision of 81.20% and recall of 71.39%, the results for this dataset are nevertheless quite promising. It should well be noted that the St-Pierre choir dataset is quite complex due to the existence of the twin pillars and the presence of many noises (folded chairs were placed against the twin pillars in addition to the presence of the iron fence on the posterior pillar). Indeed, manual segmentation and labeling took quite some time to perform the task due to these conditions. Granted, the automatic results still had remaining noises and must be cleaned \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multicolumn{2}{c}{**Point Number**} & \multicolumn{2}{c}{**Misclassed**} & \multirow{2}{*}{**True Positive**} & \multirow{2}{*}{**\%Unclassed**} & \multirow{2}{*}{**\%P**} & \multirow{2}{*}{**\%R**} & \multirow{2}{*}{**\%F1**} \\ \cline{2-2} \cline{5-8}  & **Manual** & & & & & & & & & \\ \hline K01 & 2 963 & 2 106 & 2 106 & 0 & 857 & 28.92 & 100.00 & 71.08 & 83.09 \\ \hline K02 & 2 543 & 1 819 & 1 815 & 4 & 728 & 28.63 & 99.78 & 71.37 & 83.22 \\ \hline K03 & 2 577 & 1 787 & 1 783 & 4 & 794 & 30.81 & 99.78 & 69.19 & 81.71 \\ \hline K04 & 2 379 & 1 618 & 1 618 & 0 & 761 & 31.99 & 100.00 & 68.01 & 80.96 \\ \hline K05 & 3 698 & 2 340 & 2 340 & 0 & 1 358 & 36.72 & 100.00 & 63.28 & 77.51 \\ \hline K06 & 3 440 & 2 158 & 2 158 & 0 & 1 282 & 37.27 & 100.00 & 62.73 & 77.10 \\ \hline K07 & 3 646 & 2 282 & 2 282 & 0 & 1 364 & 37.41 & 100.00 & 62.59 & 76.99 \\ \hline K08 & 3 361 & 2 117 & 2 117 & 0 & 1 244 & 37.01 & 100.00 & 62.99 & 77.29 \\ \hline  & & & & & & & & & \\ \hline  & & & & & & & & & & \\ \hline  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis on the results of step 2 for the detection and classification of columns in the Kasepuhan dataset. %P is precision, %R is recall, and %F1 is the normalized F1 score. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multicolumn{2}{c}{**Point Number**} & \multicolumn{2}{c}{**Misclassed**} & \multirow{2}{*}{**True Positive**} & \multirow{2}{*}{**\%Unclassed**} & \multirow{2}{*}{**\%P**} & \multirow{2}{*}{**\%R**} & \multirow{2}{*}{**\%F1**} \\ \cline{2-2} \cline{5-8}  & **Manual** & & & & & & & & & \\ \hline S01 & 72 587 & 54 995 & 47 709 & 7 286 & 24 878 & 34.27 & 86.75 & 65.73 & 74.79 \\ \hline S02 & 66 298 & 64 952 & 52 922 & 12 030 & 13 376 & 20.18 & 81.48 & 79.82 & 80.64 \\ \hline S03 & 74 430 & 55 979 & 50 435 & 5 544 & 23 995 & 32.24 & 90.10 & 67.76 & 77.35 \\ \hline S04 & 71 667 & 59 277 & 43 647 & 15 630 & 28 020 & 39.10 & 73.63 & 60.90 & 66.67 \\ \hline S05 & 64 893 & 54 969 & 54 343 & 626 & 10 550 & 16.26 & 98.86 & 83.74 & 90.68 \\ \hline S06 & 66 678 & 61 804 & 50 018 & 11 786 & 16 660 & 24.99 & 80.93 & 75.01 & 77.86 \\ \hline S07 & 67 316 & 75 062 & 51 996 & 23 066 & 15 320 & 22.76 & 69.27 & 77.24 & 73.04 \\ \hline S08 & 60 165 & 49 212 & 35 814 & 13 398 & 24 351 & 40.47 & 72.77 & 59.53 & 65.49 \\ \hline  & & & & & & & & & & \\ \hline  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis on the results of step 2 for the detection and classification of columns in the St-Pierre dataset. %P is precision, %R is recall, and %F1 is the normalized F1 score. further manually. However, with a fast processing time (a little under one and a half minute), this solution may prove to be very useful in performing the segmentation task, or at least provide a first approximate result. For the Valentino dataset, the unclassed rate stands at a median value of 11.84%.