## 5 Experimental Results and Discussion
### Image Matching and Pattern Recognition
The smallest number of feature points per pattern image is observed either by the experiment AGAST-BRISK-2 or by the experiment FAST-ORB-500. While the number of feature points extracted by the latter lies near the upper threshold of features set by this experiment, the number of features extracted by the AGAST detector greatly depends on the content of the pattern images (i.e., the detected corners) and differs based on it. Figure 6 illustrates clustered bar charts with the number of feature points for each pattern image, extracted by the tested detectors with different parameterization. Whereas the greatest number of feature points in the small-sized pattern images is observed for the FAST-ORB-2000 experiment, the greatest number of feature points per camera frame along with the greatest number of matches is observed by the AGAST-BRISK-3 and AGAST-BRISK-2 experiments, as outlined in Table 6. This is explained by the lack of any upper threshold for the number of feature points in the use of the AGAST detector along with the higher resolution of the camera frames compared to the resolution of the pattern images. Furthermore, the fact that the number of features along with the number of outliers significantly differ for every camera frame is due to the quite different content of each one, which depends on the depicted scene. Taking into account the fact that a MAR application should act in real-time, the image matching process between the pattern image and consecutive frames should be accomplished in the shortest time possible. Hence, a very important measurement of this research is the computational time of the image matching process, which is the most resource-demanding task of the computer vision workflow. Table 7 shows the average computational time required by the four basic steps of the image matching and outlier removal process (i.e., feature detection; feature description; main process of image matching; and homography estimation for geometric verification of matches) for each experiment  
Figure 6: Clustered bar chart illustrating the number of feature points for each pattern image used in the experiments extracted by the tested detectors with different parameterization. performed via ModAR using the low-end device. Table 8 shows the corresponding results for the mid-range device. These results were derived using 50 camera frames for each pattern image used in the experiments (in total 300 frames); the metrics have been collected using Android profiler. As far as the computational time of image matching is concerned, the results showed that the oriented FAST detector used by ORB along with the ORB descriptor required the shortest time to complete both the detection and description processes. On the other hand, the AGAST detector combined with the BRISK descriptor were significantly slower in terms of computational time required for both the detection and description processes, thus being unsuitable for real-time AR applications for both low-end and mid-range devices. Approximately the same time was required for feature detection by AGAST and feature description by BRISK. However, ORB feature description required almost 3 times more time to be accomplished compared with feature detection by oriented FAST. The AGAST-BRISK-3 experiment was the most time consuming one. The AGAST-BRISK-2 experiment was a little faster. However, both experiments were proved to be unsuitable for the ModAR prototype. The FAST-ORB-500 experiment was accomplished in the shortest time, as it required in average 0.309 s for the low-end device and 0.250 s for the mid-range device. Additionally, the FAST-ORB-2000 experiment proved to be suitable for AR augmentation, as it was accomplished in average in 0.434 s for the low-end device and 0.356 s for the mid-range device. Thus, only the combination of the oriented FAST detector