Distinctive accuracy measurement of binary descriptors in mobile augmented reality

Siok Yee Tan, Haslina Arshad and Azizi Abdullah

PLOS ONE, 2019, vol. 14, issue 1, 1-18

Abstract: Mobile Augmented Reality (MAR) requires a descriptor that is robust to changes in viewing conditions in real time application. Many different descriptors had been proposed in the literature for example floating-point descriptors (SIFT and SURF) and binary descriptors (BRIEF, ORB, BRISK and FREAK). According to literature, floating-point descriptors are not suitable for real-time application because its operating speed does not satisfy real-time constraints. Binary descriptors have been developed with compact sizes and lower computation requirements. However, it is unclear which binary descriptors are more appropriate for MAR. Hence, a distinctive and efficient accuracy measurement of four state-of-the-art binary descriptors, namely, BRIEF, ORB, BRISK and FREAK were performed using the Mikolajczyk dataset and ALOI dataset to identify the most appropriate descriptor for MAR in terms of computation time and robustness to brightness, scale and rotation changes. The obtained results showed that FREAK is the most appropriate descriptor for MAR application as it able to produce an application that are efficient (shortest computation time) and robust towards scale, rotation and brightness changes.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0207191

DOI: 10.1371/journal.pone.0207191

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