• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.79-88
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• 2011

In this paper, We propose real-time sound editing and playback systems which is based on Augmented Reality. The proposed system are composed with music maker which is based on AR maker and music board. By using music marker's contents, the proposed system selects the kinds of musical instruments and pre-defined midi track and by calculating the relative location of music marker on 2-dimensional plane, we set the spatial relative parameter in midi track. For performance evaluation, we check the jitter value of in various resolutions by using CAM which supports $1600{\pm}1200$ as the maximum resolution. As a result, when we set the configuration value of CAM as $860{\pm}600$ pixels and process two frames per minute, the success ratio of recognizing music markers and jitter values are accegnable. It can be utilized in the fields of alternative cmacine which is based on music and also be utilized in the educational aspects because child or elderly who don't know enough musical theory can easily handle it.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.247-276
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• 2009

The purpose of this essay is to explore the methodological standpoint and the meaning of 'Discourse Analysis' in policy science. I discussed it in three dimensions including: 1) the ontological point of view, 2) the epistemological perspective, and 3) researcher's position in policy research. 1) From the ontological standpoint, I explained the policy as a text, context, discourse, and ideology, that is focused on being constructed by the formative power of language. 2) The ontological standpoint produced "the argumentative turn" in the policy analysis, and many policy analysts emphasize the argumentative process of policy making and evaluation. This argumentation process includes the interpretative and critical viewpoints as well as the normative and ethical characteristics of policies in the discourse analysis. We should reexamine reality critically because discourse is ultimately influenced by the prevailing cultural and social norms. Therefore, an interpretative and critical viewpoint is an epistemological perspective in the discourse analysis. This critical approach creates an awareness of the limitations on our thinking under the particular major discourse, and requires the self-reflection within and beyond the discourse. This process leads to the human emancipation. 3) In order to achieve this emancipation, the last approach suggests that we need to scrutinize "the subject" as a researcher, who is also influenced and subjectified by the major discourse and, thus must deconstruct his or herself. Last but not least, we should emphasize the researcher's role as a listener of the minor voice(discourse) and even the silence of the clients.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.421-432
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• 2013

The purpose of this study is to develop contents and an instructional model that support children's dramatic play by integrating the robot and augmented reality technology. In order to support the dramatic play, the robot shows various facial expressions and actions, serves as a narrator and a sound manager, supports the simultaneous interaction by using the camera and recognizing the markers and children's motions, records children's activities as a photo and a video that can be used for further activities. The robot also uses a projector to allow children to directly interact with the video object. On the other hand, augmented reality offers a variety of character changes and props, and allows various effects of background and foreground. Also it allows natural interaction between the contents and children through the real-type interface, and provides the opportunities for the interaction between actors and audiences. Along with these, augmented reality provides an experience-based learning environment that induces a sensory immersion by allowing children to manipulate or choose the learning situation and experience the results. In addition, the instructional model supporting dramatic play consists of 4 stages(i.e., teachers' preparation, introducing and understanding a story, action plan and play, evaluation and wrapping up). At each stage, detailed activities to decide or proceed are suggested.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.