Machine learning schemes in augmented reality for features detection

Augmented Reality (AR) is a relatively old concept technology, which reached the large public very recently. We can use it to enhance our environments, by augmenting the image, the voice and delivering details and annotations about the surrounding space. Augmented reality (AR) is a growing field, with many diverse applications ranging from TV and film production, to industrial maintenance, medicine, education, entertainment and games. This paper presents an improved approach for image augmented-reality, by acting on two axes in the augmented reality process. First, a machine learning step is added to the detection part. Second, the registration of augmented image is processed by using the following techniques: statistical appearance models, and covariance matrices of dense image descriptors. A tuning of the used techniques and algorithms will be done in order to obtain a reliable and real-time image augmentation. We give a detailed description on how we chose the methods, and we compare our approach with other methods used in this domain. Finally, an evaluation of the proposed technique is presented as well as a performance study for a given use case.