Abstract
Based on the traditional Fast Retina Keypoint (FREAK) feature description algorithm, this paper proposed a Gravity-FREAK feature description algorithm based on Micro-electromechanical Systems (MEMS) sensor to overcome the limited computing performance and memory resources of mobile devices and further improve the reality interaction experience of clients through digital information added to the real world by augmented reality technology. The algorithm takes the gravity projection vector corresponding to the feature point as its feature orientation, which saved the time of calculating the neighborhood gray gradient of each feature point, reduced the cost of calculation and improved the accuracy of feature extraction. In the case of registration method of matching and tracking natural features, the adaptive and generic corner detection based on the Gravity-FREAK matching purification algorithm was used to eliminate abnormal matches, and Gravity Kaneda-Lucas Tracking (KLT) algorithm based on MEMS sensor can be used for the tracking registration of the targets and robustness improvement of tracking registration algorithm under mobile environment.
Highlights
With the rapid development of image processing and artificial intelligence, the conception can be realized through the combing use of different technologies and the augmented reality technology which focuses on virtual-real fusion emerged [1,36,39]
Different from the virtual reality technologies that focus on introducing users to virtual 3D scenes, the augmented reality technology emphasizes how to accurately integrate the virtual information generalized by computer into the real-world environment so that to realize the simultaneous presentation of virtual information and the real environment for the supplementation and enhancement of the real environment
They cannot be applied to mobile augmented reality systems directly as most of the mainstream mobile devices are not equipped with floating point processor (FPP), and the CPU speed and memory capacity are not able to support the devices efficiently to conduct feature extraction and position calculation of the target
Summary
With the rapid development of image processing and artificial intelligence, the conception can be realized through the combing use of different technologies and the augmented reality technology which focuses on virtual-real fusion emerged [1,36,39]. Different from the virtual reality technologies that focus on introducing users to virtual 3D scenes, the augmented reality technology emphasizes how to accurately integrate the virtual information generalized by computer into the real-world environment so that to realize the simultaneous presentation of virtual information and the real environment for the supplementation and enhancement of the real environment. The relationship between the two parts is show as Fig 1: Generally, the augmented reality system is consisted of three parts: virtual-real fusion, realtime interaction and 3D registration [2]. 3D registration, the accurate matching between virtual and real environments, is the key restraining factor of wider application of augmented reality technology.
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