Paper Info
Title
Colorcodear: Large Identifiable Colorcode-Based Augmented Reality System
Abstract
Augmented reality (AR) is widely used in various applications of computer vision, such as marker-based AR and markerless-based AR. These AR techniques are used in various fields, including industry, education, and medicine. Using marker-based AR, employees can easily perform step-by-step maintenance and repairs, and they can register parts information for large plants. However, conventional marker-based AR relies on a relatively small number of recognizable IDs compared to barcode markers. In this paper, to address the insufficient identification volume in conventional AR systems, we integrate barcode-based code technology with marker-based AR technology. Based on the results of an experiment, we applied ColorCode to our marker-based AR system. Nevertheless, difficulties arise when applying ColorCode to an AR system, owing to its recognition distance and relatively small size, compared to other AR codes. In this paper, therefore, we complemented quad detection with a tracking technique for various angles and distances, facilitating reliable recognition of the color-code-based AR system, Moreover, we added a tracking module to address the system's failure to detect markers. The experimental results demonstrate that the proposed system offers stable recognition.
Year
Venue
Field
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Computer science,Augmented reality,Artificial intelligence,Barcode,Distortion,Cybernetics,Maintenance engineering,Machine learning
DocType
ISSN
Citations 
Conference
1062-922X
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Seungho Chae1126.03
Jonghoon Seo2165.46
Yoonsik Yang3145.74
Tack-Don Han435166.39