• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3090-3102
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• 2015

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.297-304
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• 2015

Importance and necessity is enlarged due to globalization. So, English education is diverse and recently applies AR(Augmented Reality) technology to offer immersion and interest. Contents in this paper are Mobile AR contents for training infant English cognition. Our system displays teaching aid objects into 3D models, lets infants listen to English conversation and teaches them everyday English like a play. Considering infants that like games, this system adds an English game about each object of teach aids. To attract infants and parents interest persistingly, We give them immersion and interest by suggesting a different event at each step.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.98-102
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• 2023

This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

• Korean Journal of Computational Design and Engineering
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• v.20 no.1
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• pp.55-64
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• 2015

The earthquake is a natural disaster causing loss of life or property damage and happens more often in Korea recently. Moreover, considering the increase of massive buildings, it is required to predict and visualize the information of the vibration in a building. In this paper, we developed a prototype framework to visualize the displacement information in the AR environments. In order to avoid the irregular halts of the scene and the unnatural distortion of the object, this framework uses the synchronization method at the scene update time and the interpolation of the sensor data for the displacement of vertices. In addition, we studied displacement estimation methods with the acceleration data to extend this framework to the system with accelerators.

• Journal of KIBIM
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• v.10 no.4
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• pp.60-69
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• 2020

Allowing on-site workers to access information of the construction process can enable task control, data integration, material and resource control. However, in the current practice of the construction industry, the existing methods and scope is quite limited, leading to inefficient management during the construction process. In this research, by adopting cutting edge technologies such as Augmented Reality(AR), digital twins, deep learning and computer vision with wearable AR devices, the authors proposed an AR visualization framework made of virtual components to help on-site workers to obtain information of the construction process with ease of use. Also, this paper investigates wearable AR devices and object detection algorithms, which are critical factors in the proposed framework, to test their suitability.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.44-48
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• 2023

Augmented Reality (AR) is an emerging technology and a vibrant field, it has become common in application development, especially in smartphone applications (mobile phones). The AR technology has grown increasingly during the past decade in many fields. Therefore, it is necessary to determine the optimal approach to building the final product by evaluating the performance of each of them separately at a specific task. In this work we evaluated overall CPU and RAM performance for several types of Markerless Augmented Reality applications by using a multiple-objects in mobile development. The results obtained are show that the objects with fewer number of vertices performs steady and not oscillating. Object was superior to the rest of the others is sphere, which is performs better values when processed, its values closer to the minimum CPU and RAM usage.

• Architectural research
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• v.19 no.1
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• pp.1-6
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• 2017

Over the past decade, building information modeling (BIM) has gained a foothold in the construction sector. However, as digital data generated in the virtual space of a computer environment, BIM-based data have an inherent limitation in their application efficacy under field conditions. To overcome this, the present study employs augmented reality (AR) to reduce the discrepancy between the digital data generated in virtual spaces and real-world conditions. We assessed the potential applicability of an AR-based interface by analyzing existing examples of Apple, Google, and Facebook, which reflect the recent developments of technologies focusing on user experience. We then proceeded with an AR study, restricting the scope of application to a mobile environment in which an efficient information transmission between a digital model and real life can take place. Object-oriented software engineering was employed to ensure an efficient implementation of a BIM-based AR system for campus space management (CSM) in a mobile environment. Finally, we conducted a module test to check the reliability of the CSM method by using an AR-based mobile system with a prototype of the model used in university campuses, and extracted and itemized the supplementary requirements for CSM by using BIM tools for running AR applications.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.829-834
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• 2008

Augmented reality makes the relationship between real world and virtual world. The basic goal of an AR system is to enhance the user's perception of and interaction with the real world through supplementing the real world with 3D virtual objects that appear to coexist in the same space as the real world. ARToolkit is widely used toolkit to develop any AR application. However, to do so, a lot of researcher faced many challenges and limitations. In this paper we comprehensively review some selected challenging issues using ARToolkit to develop AR system. And then, we implement a real application for augmented text book with sounds and 3D virtual objects which we called here AR Traffic Book concerning these challenging issues by ARToolkit. In this paper our foremost approaches on the most common challenging issues such as virtual object rendering, camera calibration and tracking.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.4
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• pp.143-147
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• 2011

The system using augmented reality can save the time and cost. It is verified in various fields under the possibility of a technology by solving unrealistic feeling in the virtual space. Therefore, augmented reality has a variety of the potential to be used. Generally, multimodal senses such as visual/auditory/tactile feed back are well known as a method for enhancing the immersion in case of interaction with virtual object. By adapting tangible object we can provide touch sensation to users. a 3D model of the same scale overlays the whole area of the tangible object; thus, the marker area is invisible. This contributes to enhancing immersive and natural images to users. Finally, multimodal feedback also creates better immersion. In this paper, sound feedback is considered. By further improving immersion learning augmented reality for children with the initial step learning content is presented. Augmented reality is in the intermediate stages between future world and real world as well as its adaptability is estimated more than virtual reality.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.