Distinctive accuracy measurement of binary descriptors in mobile augmented reality

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). A...

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Bibliographic Details
Published in:PloS one 2019-01, Vol.14 (1), p.e0207191-e0207191
Main Authors: Tan, Siok Yee, Arshad, Haslina, Abdullah, Azizi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Augmented reality
Binary asteroids
Biology and Life Sciences
Brightness
Computer and Information Sciences
Computing time
Efficiency
Engineering and Technology
Floating point arithmetic
Image Interpretation, Computer-Assisted
Information science
Internet of Things
Personal computers
Physical Sciences
Real time
Research and Analysis Methods
Researchers
Robustness (mathematics)
Rotation
Sensors
Smartphones
State of the art
Time Factors
Virtual Reality
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0207191