<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The article reports on the investigation of augmented reality system which is designed for identification and augmentation of 100 different square markers. Marker recognition efficiency was investigated by rotating markers along x and y axis directions in range from −90° to 90°. Virtual simulations of four environments were developed: a) an intense source of light, b) an intense source of light falling from the left side, c) the non-intensive light source falling from the left side, d) equally falling shadows. The graphics were created using the OpenGL graphics computer hardware interface; image processing was programmed in C++ language using OpenCV, while augmented reality was developed in Java programming language using NyARToolKit. The obtained results demonstrate that augmented reality marker recognition algorithm is accurate and reliable in the case of changing lighting conditions and rotational angles - only 4 % markers were unidentified. Assessment of marker recognition efficiency let to propose marker classification strategy in order to use it for grouping various markers into distinct markers’ groups possessing similar recognition properties.</p></abstract>

The article reports on the investigation of augmented reality system which is designed for identification and augmentation of 100 different square markers. Marker recognition efficiency was investigated by rotating markers along x and y axis directions in range from −90° to 90°. Virtual simulations of four environments were developed: a) an intense source of light, b) an intense source of light falling from the left side, c) the non-intensive light source falling from the left side, d) equally falling shadows. The graphics were created using the OpenGL graphics computer hardware interface; image processing was programmed in C++ language using OpenCV, while augmented reality was developed in Java programming language using NyARToolKit. The obtained results demonstrate that augmented reality marker recognition algorithm is accurate and reliable in the case of changing lighting conditions and rotational angles - only 4 % markers were unidentified. Assessment of marker recognition efficiency let to propose marker classification strategy in order to use it for grouping various markers into distinct markers’ groups possessing similar recognition properties.

An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm

Doctoral Student, Vilnius Gediminas Technical University

Electrical, Control and Communication Engineering

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm"}

The article reports on the investigation of augmented reality system which is designed for identification and augmentation of 100 different square markers....

An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm

Online veröffentlicht: 17. Mai 2014

Seitenbereich: 54 - 60

DOI: https://doi.org/10.2478/ecce-2014-0008

Schlüsselwörter
Computers and information processing, Computational efficiency, Augmented reality, Image recognition, Open source software

© Riga Technical University

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

An Efficiency Analysis of Augmented Reality Marker Recognition Algorithm

Online veröffentlicht: 17. Mai 2014

Seitenbereich: 54 - 60

DOI: https://doi.org/10.2478/ecce-2014-0008

SchlüsselwörterComputers and information processing, Computational efficiency, Augmented reality, Image recognition, Open source software

© Riga Technical University

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Schlüsselwörter
Computers and information processing, Computational efficiency, Augmented reality, Image recognition, Open source software