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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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| Published in: | PloS one 2019-01, Vol.14 (1), p.e0207191-e0207191 |
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| creator | Tan, Siok Yee Arshad, Haslina Abdullah, Azizi |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0207191 |
| format | article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0207191</identifier><identifier>PMID: 30605474</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2019-01, Vol.14 (1), p.e0207191-e0207191</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Augmented reality</subject><subject>Binary asteroids</subject><subject>Biology and Life Sciences</subject><subject>Brightness</subject><subject>Computer and Information Sciences</subject><subject>Computing time</subject><subject>Efficiency</subject><subject>Engineering and Technology</subject><subject>Floating point arithmetic</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Information science</subject><subject>Internet of Things</subject><subject>Personal computers</subject><subject>Physical Sciences</subject><subject>Real time</subject><subject>Research and Analysis 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30605474</pmid><doi>10.1371/journal.pone.0207191</doi><tpages>e0207191</tpages><orcidid>https://orcid.org/0000-0003-3566-9938</orcidid><oa>free_for_read</oa></addata></record> |
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| 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 |
| title | Distinctive accuracy measurement of binary descriptors in mobile augmented reality |
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