## 5 Experimental Results and Discussion
### Image Matching and Pattern Recognition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Experiment**} & \multicolumn{1}{c}{**AGAST-BRISK-3**} & \multicolumn{1}{c}{**AGAST-BRISK-2**} & \multicolumn{1}{c}{**FAST-ORB-**} & \multicolumn{1}{c}{**FAST-ORB-**} \\  & **BRISK-3** & **BRISK-2** & **2000** & **500** \\ \hline \multirow{3}{*}{Number of features in pattern image} & BOTERO & 248 & 231 & 823 & 495 \\  & SCYTHE & 544 & 540 & 1189 & 430 \\  & SURVEYING & 781 & 761 & 1595 & 485 \\  & JEFFERS & 564 & 553 & 1079 & 437 \\  & PAINTING & 174 & 168 & 604 & 400 \\  & GRAFFIT & 603 & 588 & 1279 & 475 \\  & Number of features per frame & 500–2500 & 500–2500 & 300–2000 & 100–500 \\  & Number of inliers & 10–300 & 10–250 & 10–200 & 10–100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of features extracted in each pattern image, number of features per frame and number of inliers for the experiments performed using the ModAR prototype. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Stage of Computer**} & \multicolumn{1}{c}{**AGAST-BRISK-3**} & \multicolumn{1}{c}{**AGAST-BRISK-2**} & \multicolumn{1}{c}{**FAST-ORB-2000**} & \multicolumn{1}{c}{**FAST-ORB-500**} \\ \cline{2-9}  & **Time (s)** & **CPU (\%)** & **Time (s)** & **CPU (\%)** & **Time (s)** & **CPU (\%)** & **Time (s)** & **CPU (\%)** \\ \hline Feature detection & 2.425 & 49.47 & 2.296 & 47.73 & 0.086 & 12.51 & 0.069 & 12.02 \\ Feature description & 2.337 & 45.91 & 2.229 & 46.69 & 0.230 & 32.09 & 0.189 & 34.29 \\ Image matching & 0.029 & 0.53 & 0.026 and ORB descriptor were further evaluated in ModAR. They achieve about 90% reduction in the computational time of image matching compared to the corresponding time required by the AGAST detector and BRISK descriptor. The rest tasks that complete the computer vision workflow (mainly the reading of the extracted feature points of the pattern image and their descriptors in each frame, along with the basic steps of pattern recognition and camera pose estimation) were completed in less than 0.250 s for all experiments. The total computational time required by the four basic steps of image matching for each experiment performed via ModAR is illustrated in the form of clustered bar charts in Figure 7.  
Similar results were achieved regarding CPU usage. Specifically, the amount of CPU time that the application process uses for each method as a percentage of the total available CPU time of the device concerning the computer vision workflow is shown in Table 6, for the low-end device and in Table 7, for the mid-range device. The feature detection and description tasks implemented by the time-consuming experiments AGAST-BRISK-3 and AGAST-BRISK-2 correspond to approximately 95% of CPU usage out of the total CPU usage of the computer vision workflow, whereas the corresponding CPU usage percentage for feature detection and description by oriented FAST and ORB reaches approximately 50%. The detection of correspondences along with homography estimation are neither time-consuming tasks nor CPU-demanding, as shown in Tables 6 and 7. As far as the pattern recognition results are concerned, the correspondences detected by the ModAR prototype generally lead to successful pattern object recognition in all the aforementioned experiments. Both AGAST-BRISK and FAST-ORB yield successful pattern recognition results regardless of the rotation of the pattern object. Whereas the use of the AGAST detector in combination with the BRISK descriptor yields better results in cases of strong change of scale between the pattern object and its appearance in the camera frame, the computational time required by AGAST feature detection and BRISK description is prohibitive for AR applications, as discussed in the previous paragraphs.