A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions

Until today, various approaches have been proposed in order to create Augmented Reality (AR) environment where virtual-real integration takes place. One of these approaches is vision-based model, and it is divided into two branches as Marker-Based Augmented Reality (MBAR) and Markerless Augmented Reality (MAR). In the use of MBAR model, a reference image is introduced to the system before, and when this image enters the camera view, an AR environment is created. However, in MAR model, no image is introduced to the system before. Instead, it uses natural  characteristics present in the image, such as edges, corners, and geometrical shapes to create an AR environment. In order to use MAR model, it is necessary to use algorithms which require high processing power and memory capacity. Within the scope of this study, MAR model was chosen as reference and an evaluation on combinations of descriptive extractors (such as ORB, SIFT, and SURF) and matchers (such as Bruteforce, Bruteforce-Hamming, and Flannbase) was presented. In this context, it was aimed that we could obtain knowledge about i) the number of key points and detection time with the use of different descriptor extractors, and ii) the number of matching key points and the amount of positional deviation of a virtual object placed on a real world scene with the use of different matchers. In line with this goal, analyses were made, using different image scales and brightness levels on both PC and mobile platforms. Results showed that, for both platforms, combinations using ORB method could work faster with less deviation than the combinations using other methods in all conditions. In addition, RANSAC algorithm was also used to reduce the total mean deviation ratio, and it was seen that the rate could be reduced from 70% to 4.5%..