## 3 Experimental Results  
For the testing and validation of the proposed sensor we used 100 boxes from 60 images (Figure 7) for the learning phase and 200 images for the operating phase. As mentioned in Section 2, 50 boxes (10 for _OD_, 10 for _MA_, 10 for _EX_, 10 for _HE_, and 10 for _NR_; Figure 8) were used in feature selection and another 50 boxes were used in classifier implementation. A box dimension of 128 \(\times\) 128 pixels was used for STARE and a box dimension of 256 \(\times\) 256 pixels was used for MESSIDOR. The sliding step sizes (BDS) were 10 pixels for the STARE DB and 15 pixels for the MESSIDOR DB. In BVS, the sliding step size was 1 pixel for both. The same features were selected both in the STARE case and the MESSIDOR case. Examples of feature calculations for _OD_ class representations are presented in Table 2 (36 features) and Table 3 (_LBP_ feature). The last column contains the average values of the row. The results concerning the representatives for the classes _OD_, _MA_, _EX_, and _HE_ are presented in Table 4. The features from CF (Table 1) were tested on R, G, B, and H color components and, in concordance with the selection criterion, the selected features and the corresponding color channels are marked with green in Table 4.  
The confusion matrices are calculated using the set _LS2_ for all features and all classes. Some examples of _CMs_ are presented in Figure 9. As can be observed, the values greater than 8 (greater than 0.75\(C^{i}\)_a_--see feature selection from the Section 2.3.2) are highlighted with green. For example, according to Figure 9a, _ImG_ is a feature selected for _OD_ and _EX_ detection. Similarly, Figure 9b determines the classifiers which use _ConG_, Figure 9c determines the classifiers which use _EnG_, Figure 9d determines the classifiers which use _HomG_, Figure 9f determines the classifiers which use _LBPH_, Figure 9g determines the classifiers which use _FDG_, and Figure 9h determines the classifiers which use _LR_. It can be observed in Figure 9e that _EntG_ is not a proper feature for any class. Figure 8: Boxes of RoIs for feature calculation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Boxes/Features** & **LB1\_1** & **LB2\_1** & **LB3\_1** & **LB4\_1** & **LB5\_1** & **LB6\_1** & **LB7\_1** & **LB8\_1** & **LB9\_1** & **LB10\_1** & **Rep.** & **Repr.** & **Nation** & **Value** \\ \hline _lmR_ & 0.974 & 0.985 & 0.997 & 0.853 & 0.910 & 0.956 & 0.987 & 0.801 & 0.966 & 0.836 & \(\overline{ImR}_{OD}\) & 0.926 \\ \(\textbf{lmG}\) & 0.543 & 0.528 & 0.567 & 0.564 & 0.521 & 0.542 & 0.515 & 0.555 & 0.594 & 0.585 & \(\overline{ImR}_{OD}\) & 0.581 \\ _lmB_ & 0.107 & 0.119 & 0.053 & 0.094 & 0.115 & 0.051 & 0.129 & 0.015 & 0.094 & 0.101 & \(\overline{ImB}_{OD}\) & 0.088 \\ _lmH_ & 0.085 & 0.085 & 0.084 & 0.088 & 0.084 & 0.063 & 0.064 & 0.076 & 0.083 & 0.084 & \(\overline{ImB}_{OD}\) & 0.080 \\ _ConR_ & 0.706 & 0.209 & 0.252 & 0.005 & 0.000 & 0.000 & 0.099 & 0.277 & 0.280 & 0.112 & \(\overline{ConR}_{OD}\) & 0.194 \\ _ConR_ & 0.456 & 0.426 & 0.488 & 0.470 & 0.471 & 0.392 & 0.428 & 0.417 & 0.408 & 0.476 & \(\overline{ConR}_{OD}\) & 0.443 \\ _ConB_ & 0.128 & 0.036 & 0.275 & 0.131 & 0.151 & 0.419 & 0.097 & 0.269 & 0.142 & 0.040 & \(\overline{ConR}_{OD}\) & 0.169 \\ _ConH_ & 0.024 & 0.034 & 0.012 & 0.050 & 0.031 & 0.120 & 0.701 & 0.305 & 0.023 & 0.023 & \(\overline{ConR}_{OD}\) & 0.132 \\ _EntR_ & 0.764 & 0.811 & 0.853 & 0.870 & 0.438 & 0.998 & 0.928 & 0.132 & 0.379 & 0.120 & \(\overline{EntR}_{OD}\) & 0.619 \\ _EntG_ & 0.804 & 0.725 & 0.734 & 0.691 & 0.999 & 0.763 & 0.646 & 0.709 & 0.733 & 0.664 & \(\overline{EntR}_{OD}\) & 0.746 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Exemplification of feature calculation for the \(OD\) class (marked with orange are the features selected for \(OD\)). Figure 7: Examples of images from MESIDOR (top) and STARE (bottom) databases for the learning phase. Table 5 presents the traces of all confusion matrices associated with features (the rows represent such _CM_ traces).