• Journal of Multimedia Information System
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• v.7 no.1
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.838-848
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• 2021

Parents' influence on children's development is generally considered essential. This paper attempts to explore the role of AR in preschool education from the perspective of parents, aiming to help parents better understand the impact of children's use of augmented reality in preschool education. The subjects were parents of children in the preschool age range (3-6 years old), and the experimental equipment was AR cognitive cards. In order to extract parents' views on AR, five parents were invited to conduct an experiment with their children using AR cognition cards, and then an open interview survey was conducted. In the second experiment, the answers obtained from the first experiment were sorted out and formed a questionnaire to conduct a closed-book survey. It shows that parents are satisfied with the characteristics of AR to assist their children's learning. At the same time, parents also value technology, usage management and playing environment. AR can stimulate children's learning initiative. Children like to use AR, AR is suitable for learning, make parents satisfied. But even if AR is suitable for learning, parents will control the time their children use it.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.485-486
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• 2019

Simulated driving behavior is an important aspect of realistic simulation systems. To simulate natural driving behavior, this paper proposes an imitation learning method based on active learning that combines demonstration and experience. Driving demonstrations are collected from human drivers in a driving simulator. A driving behavior policy is learned from these demonstrations. The driving demonstration dataset is augmented with new demonstrations that the original demonstrations did not contain, in the form of behaviors from another driving behavior policy learned from experience. The final driving behavior policy is learned from an augmented demonstration dataset.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.30-37
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• 2021

Accurate 3D object tracking with camera images is a key enabling technology for augmented reality applications. Motivated by the impressive success of convolutional neural networks (CNNs) in computer vision tasks such as image classification, object detection, image segmentation, recent studies for 3D object tracking have focused on leveraging deep learning. In this paper, we review deep learning approaches for 3D object tracking. We describe key methods in this field and discuss potential future research directions.

• Journal of the Korean Chemical Society
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• v.68 no.2
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• pp.93-106
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• 2024

This study investigated differences in presence, immersion, and situational interest in small group learning using augmented reality, based on the degree of tool sharing. 84 eighth-grade students participated in small groups of four. Each group was randomly assigned to one of three environments based on marker and device sharing: the shared environment (shared marker and device usage), the mixed environment (shared marker and individual device usage), and the individual environment (individual marker and device usage). Small group learning using augmented reality was conducted for three class periods, focusing on the "Characteristics of Matter" unit. One-way ANOVA results for the dependent variables revealed that, compared to the shared environment, presence and situational interest were significantly higher in the mixed environment, while immersion and situational interest were significantly higher in the individual environment. MANOVA results for the sub-components of each dependent variable showed significant differences in realness for presence, antecedents and experiences for immersion, and instant enjoyment, novelty, and total interest for situational interest. Analysis of interviews and classroom observations indicated that students in shared and individual environments tended to use their devices individually when utilizing augmented reality. However, in mixed environments, students showed a tendency to use their devices collaboratively, leading to more active interactions. Based on these findings, environments for using tools to enhance the effectiveness of small group learning using augmented reality are discussed.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.125-137
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• 2023

This study analyzed the types of knowledge building discourse and knowledge building processes in small group learning using augmented reality. Eight 8th grade students took classes using augmented reality in solubility, boiling and melting points. These classes were carried out twice and all the classes were videotaped and recorded. Every student participated in a semi-structured interview. In the types of knowledge building discourse, the proportion of knowledge sharing and knowledge construction was similar. Beneath the knowledge sharing, the proportion of introductory level discussion was higher than identifying key elements of augmented reality. Recalling existing knowledge rarely appeared. Under the knowledge construction, the proportion of advanced level discussion was the highest and the proportion of sharing and critiquing ideas at a different level and efforts to rise above current levels of explanation was similar. The introductory level discussion and identifying key elements of augmented reality were developed into efforts to rise above current levels of explanation and sharing and critiquing ideas at a different level. Visualized results of knowledge building processes showed all the students' graph drew an upward curve, though cumulative number of impact value was different by each student. As a result of the study, effective ways of improving small group learning using augmented reality are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.257-263
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• 2011

Recently as AR(Augmented Reality) is focus of attention, AR is applied to various fields and is expected its valuable use. In this paper, we implemented the system based on Goblin XNA which supports high resolution model file and higher AR. We confirmed the relation of model output among the number of marker, the location and changes of camera distance. And we produced the infantile studying contents using AR and embodied. In implemented contents, we showed the familiar character to infants on each page marker. As the result of it, we can raise their concentration and at a time studying supporters can use the contents easily as well. Also we put 3 marker on each page of contents to recognize it smoothly in case one part of it is hidden by any obstacle. Finally we maximized the learning effect such as presence and immersion in studying through reinforcing 3D models according to the every situation.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.34-41
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• 2023

In recent years, as the range of human activities has increased, the introduction of alien species has become frequent. Among them, raccoons have been designated as harmful animals since 2020. Raccoons are similar in size and shape to raccoon dogs, so they generally need to be distinguished in capturing them. To solve this problem, we use VGG19, ResNet152V2, InceptionV3, InceptionResNet and NASNet, which are CNN deep learning models specialized for image classification. The parameters to be used for learning are pre-trained with a large amount of data, ImageNet. In order to classify the raccoon and raccoon dog datasets as outward features of animals, the image was converted to grayscale and brightness was normalized. Augmentation methods were applied using left and right inversion, rotation, scaling, and shift to create sufficient data for transfer learning. The FCL consists of 1 layer for the non-augmented dataset while 4 layers for the augmented dataset. Comparing the accuracy of various augmented datasets, the performance increased as more augmentation methods were applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.241-244
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• 2014

In this paper, we apply the learning of the mobile robot insect augmented reality Digilog Book. In the era of electronic, book written in paper space just have moved to virtual reality space. The virtual reality, constraints spatial and physical, in the real world, it is a technique that enables to experience indirectly situation not experienced directly as user immersive experience type interface. Applied to the learning robot Digilog Book that allows the fusion of paper analog and digital content, using the augmented reality technology, to experience various interactions. Apply critical elements moving, three-dimensional images and animation to enrich the learning, for easier block assembly, designed to grasp more easily rank order between the blocks. Anywhere at any time, is capable of learning of the robot in Digilog Book to be executed by the mobile phone in particular.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.341-350
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• 2022

In this paper, we propose a deep learning based on-device augmented reality (AR) system in which multiple input images are used to implement the correct occlusion in a real environment. The proposed system is composed of three technical steps; camera pose estimation, depth estimation, and object augmentation. Each step employs various mobile frameworks to optimize the processing on the on-device environment. Firstly, in the camera pose estimation stage, the massive computation involved in feature extraction is parallelized using OpenCL which is the GPU parallelization framework. Next, in depth estimation, monocular and multiple image-based depth image inference is accelerated using the mobile deep learning framework, i.e. TensorFlow Lite. Finally, object augmentation and occlusion handling are performed on the OpenGL ES mobile graphics framework. The proposed augmented reality system is implemented as an application in the Android environment. We evaluate the performance of the proposed system in terms of augmentation accuracy and the processing time in the mobile as well as PC environments.