• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.25-27
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• 2021

With the recent development of deep learning technology, computer vision-based AI technologies have been studied to analyze the abnormal behavior of objects in image information acquired through CCTV cameras. There are many cases where surveillance cameras are installed in dangerous areas or security areas for crime prevention and surveillance. For this reason, companies are conducting studies to determine major situations such as intrusion, roaming, falls, and assault in the surveillance camera environment. In this paper, we propose a real-time abnormal behavior analysis algorithm using object detection and tracking method.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Cartoon and Animation Studies
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• s.49
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• pp.697-712
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• 2017

In recent years, film and animation for cinematic release have embraced stereoscopic vision and the three-dimensional depth it creates for the viewer. The maturation of consumer-level virtual reality (VR) technology simultaneously spurred a wave of media productions set within 3D space, ranging from computer games to pornographic videos, to Academy Award-nominated animated VR short film Pearl. All of these works rely on stereoscopic fusion through stereopsis, that is, the perception of depth produced by the brain from left and right images with the amount of binocular parallax that corresponds to our eyes. They aim to emulate normal human vision. Within more experimental practices however, a fully rendered 3D space might not always be desirable. In my own abstract animation work, I tend to favour 2D flatness and the relative obfuscation of spatial relations it affords, as this underlines the visual abstraction I am pursuing. Not being able to immediately understand what is in front and what is behind can strengthen the desired effects. In 2015, Jeffrey Shaw challenged me to create a stereoscopic work for Animamix Biennale 2015-16, which he co-curated. This prompted me to question how stereoscopy, rather than hyper-defining space within three dimensions, might itself be used to achieve a confusion of spatial perception. And in turn, how abstract and experimental moving image practices can benefit from stereoscopy to open up new visual and narrative opportunities, if used in ways that break with, or go beyond stereoscopic fusion. Noteworthy works which exemplify a range of non-traditional, expanded approaches to binocular vision will be discussed below, followed by a brief introduction of the stereoscopic animation loop III=III which I created for Animamix Biennale. The techniques employed in these works might serve as a toolkit for artists interested in exploring a more experimental, expanded engagement with stereoscopy.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.394-401
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• 2018

Objectives: Augmented reality (AR) technology has become rapidly available and is suitable for various medical applications since it can provide effective visualization of intricate anatomical structures inside the human body. This paper describes the procedure to develop an AR app with Unity3D and Vuforia software development kit and publish it to a smartphone for the localization of critical tissues or organs that cannot be seen easily by the naked eye during surgery. Methods: In this study, Vuforia version 6.5 integrated with the Unity Editor was installed on a desktop computer and configured to develop the Android AR app for the visualization of internal organs. Three-dimensional segmented human organs were extracted from a computerized tomography file using Seg3D software, and overlaid on a target body surface through the developed app with an artificial marker. Results: To aid beginners in using the AR technology for medical applications, a 3D model of the thyroid and surrounding structures was created from a thyroid cancer patient's DICOM file, and was visualized on the neck of a medical training mannequin through the developed AR app. The individual organs, including the thyroid, trachea, carotid artery, jugular vein, and esophagus were localized by the surgeon's Android smartphone. Conclusions: Vuforia software can help even researchers, students, or surgeons who do not possess computer vision expertise to easily develop an AR app in a user-friendly manner and use it to visualize and localize critical internal organs without incision. It could allow AR technology to be extensively utilized for various medical applications.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.55-62
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• 2024

In this paper, we propose a thin-film hydroponic plant cultivator using HMI display and IoT technology. Existing plant cultivators were difficult to manage due to soil-based cultivation, and it was difficult to optimize environmental conditions due to the open cultivation environment. In addition, there are problems with plant cultivation as immediate control is difficult and growth of plants is delayed. To solve this problem, a cultivation environment was established by connecting the MCU and sensors, and the environment information could be checked and quickly controlled by linking with the HMI display. Additionally, a case was applied to minimize changes in environmental information. Implementation of a thin-film hydroponic cultivation system made soil management easier, improved functionality through operation and control, and made it easy to understand environmental information through the display. The effectiveness of rapid growth was confirmed through crop cultivation experiments in existing growers and hydroponic growers. Future research directions will include optimizing growth information by transmitting and storing cultivation environment information and linking and comparing growth information using vision cameras. It is expected that this will enable efficient and stable plant cultivation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.55-61
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• 2020

With the development of deep learning technology and advances in computing power, video-based research is now gaining more and more attention. Video data contains a large amount of temporal and spatial information, which is the biggest difference compared with image data. It has a larger amount of data. It has attracted intense attention in computer vision. Among them, motion recognition is one of the research focuses. However, the action recognition of human in the video is extremely complex and challenging subject. Based on many research in human beings, we have found that artificial intelligence-like attention mechanisms are an efficient model for cognition. This efficient model is ideal for processing image information and complex continuous video information. We introduce this attention mechanism into video action recognition, paying attention to human actions in video and effectively improving recognition efficiency. In this paper, we propose a new 3D residual attention network using convolutional neural network based on two attention models to identify human action behavior in the video. An evaluation result of our model showed up to 90.7% accuracy.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.503-510
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• 2018

Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.667-670
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• 2005

As security and surveillance have become the center of interest, remote controlled CCTV(Closed-Circuit Television) market has been formed while rapid development of digital image compression technology and Internet triggered the advent of web cameras. The characteristic of web camera is that it can provide users with higher quality image than CCTV at any place where Internet access is available. However, As for the system administrator, the existing web camera have disadvantage in that they allows users only. who are connected to the server of the web camera, to see the image from it. In this paper, in order to make up for this defect, designed multi-vision interface showing multi images on single screen and, for the purpose of the improvement in efficiency, the functions of saving images and of scheduling the time to save the images.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.332-346
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• 2022

Digital transformation represents one of the main obstacles facing several government, private, and non-profit sectors that help stabilize digital transformation in the Arabic region. One of the helpful ways to improve the level of freedom, productivity, and flexibility among employees to accept the BYOD approach is using their own devices to perform their work both in and outside the workplace. This study focuses to present the differences between the main three economic sectors, which represent the most important pillars of the economy in Saudi Arabia within the Kingdom's Vision 2030. BYOD also has great importance to the stakeholders for raising their awareness by expressing the implications, if the concept of BYOD is widely and correctly adopted. The study uses the diffusion of innovation (DOI) framework and quantitative analysis data to determine the main dimensions and important factors that help increase the awareness of the target audience. The number of participants in this study was 830, and the participants are mixing between the government, private, and non-profit sectors. The main findings showed a significant impact of several factors such as the importance of knowledge, ease of use, employee satisfaction, risk awareness, and attention to increase the level of acceptance in three main sectors study for using the BYOD approach widespread and professional use.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.