ALGORITM SCENE RECOGNITION FOR AUGMENTED REALITY SYSTEMS
The article considers the technology of critical point recognition in object images taken from a video portion for a mobile system of augmented reality. The authors suggest of determining and matching the descriptors of critical points on the object images with further possibility of recognition system learning on the basis of using a forest of random trees. The study compares the existing methods point feature recognition, such as SIFT, SURF and RIFF, and describes their drawbacks. In order to increase the quality of the method of recognizing the objects with isolated point features on mobile devices the researchers suggest a method on the basis of a forest of random trees. The general idea of the method is recognition of objects on the basis of Bayes classifier distribution statistics on possible descriptors matching. There are two structures of random tree forests suggested: basic and augmented. The authors conducted comparative analytical and experimental research of the existing and the developed methods of object recognition with detected point features on the basis of a forest of random trees. It is shown that the application of the developed method on the basis of random trees lead to a better recognition quality value in conditions of higher requirements to random-access memory and minimal increase of sytem work time.