• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.638-653
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• 2022

In this paper, we propose the new method of creating 3D virtual-human reflecting the pattern of clothes worn by the person in the high-resolution whole body front image and the body shape data about the person. To get the pattern of clothes, we proceed Instance Segmentation and clothes parsing using Cascade Mask R-CNN. After, we use Pix2Pix to blur the boundaries and estimate the background color and can get UV-Map of 3D clothes mesh proceeding UV-Map base warping. Also, we get the body shape data using SMPL-X and deform the original clothes and body mesh. With UV-Map of clothes and deformed clothes and body mesh, user finally can see the animation of 3D virtual-human reflecting user's appearance by rendering with the state-of-the game engine, i.e. Unreal Engine.

• The Journal of Korean Philosophical History
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• no.32
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• pp.113-159
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• 2011

The self-segmentation of Seoin(西人) School and the appearance of Soron(少論) School in the 17th century Joseon society has an important meaning in the political history. Soron School(少論學派) had quite different line in their thought and public position from the Noron(老論) School represented by Song Shi-yeol. With such thought and position, Soron School added a vitality to the barren climate of Joseon intellectual society and soon became an important school of it. Especially, the senior leaders of early Soron School shared the culture of discussion and dialogue which was represented by the phrase, "the forest of debate and the hill of argument". Accordingly, Soron School could form an unique academic tradition which was different from other schools. For instance, Park Se-dang(朴世堂), Nam Gu-man(南九萬), Yoon Jeung(尹拯) and Park Se-chae(朴世采) had relatively flexible Conception of Heresy; which was in the same context with the academic position of Soron School that allowed diverse approaches to truth each as the path of justice. Furthermore, the leaders of Soron School continued dialogue and discussion on the wide range of current issues. They deeply sympathized with the Tangpyeong-ron(蕩平論), which was to appoint important government positions equally from multiple schools. They thought that the Tangpyeong-ron(蕩平論) was to avoid the harmful influence of conflicts between schools, the Bungdang(朋黨), and tried to tune and compromise the different opinions of different schools. In fact, the "Hwanggeuk Tangpyeong-ron" (皇極蕩平論) submitted by Park Se-chae was the summarization of the discussions shared by the Soron School leaders. Consequently, the seniors of Soron School practiced their philosophy that "the true academic practice is the communication" by keeping the dialogue with other schools while respecting the difference of each other, rather than staying comfortably in the closed academic fence of uniformity. Such communicative academic practice adopting discussion and dialogue had been succeeded to the second generation of Soron School and it remained as an unique academic tradition of Soron School.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.25-41
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• 2014

Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of "topic analysis" is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues. The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity. In this study, to overcome these limitations without having to analyze the entire period's documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issue's life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.