• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.186-198
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• 2021

Security control using surveillance cameras is established when people observe all surveillance videos directly. However, this task is labor-intensive and it is difficult to detect all abnormal situations. In this paper, we propose a deep neural network model, called AT-Net, that automatically detects abnormal situations in the surveillance video, and introduces an automatic video surveillance system developed based on this network model. In particular, AT-Net alleviates the ambiguity of existing abnormal situation detection methods by mapping features representing relationships between people and objects in surveillance video to the new tensor structure based on sparse coding. Through experiments on actual surveillance videos, AT-Net achieved an F1-score of about 89%, and improved abnormal situation detection performance by more than 25% compared to existing methods.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.78-87
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• 1996

In this paper, a remeshing criterion has been suggested in order to order to automate the remeshing decision during finite element analysis of metal forming. In order to use for the remeshing decision, two of remeshing criteria have been investigated. One is the use of error estimates based on errors in stresses and strain rate of the finite element solution and the other is the use of geometric characterisreics of distorted elements. As a result, the strain rate error estimate in power norm based on the former is found to give more valuable information about remeshing decision than the ones based on the latter. Examples are given to demon- strate the usefulness of the suggested eroor estimate as a remeshing criterion.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.19-26
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• 2008

This paper is related to a new technology allowing a user to have a 360 degree viewpoint of the virtual wearing object, and to an object VR(Virtual Reality)-based 2D virtual textile wearing system using viewpoint vector estimation and textile texture mapping. The proposed system is characterized as capable of virtually wearing a new textile pattern selected by the user to the clothing shape section segmented from multiview 2D images of clothes model for object VR, and three-dimensionally viewing its virtual wearing appearance at a 360 degree viewpoint of the object. Regardless of color or intensity of model clothes, the proposed system is possible to virtually change the textile pattern with holding the illumination and shading properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple textile pattern combinations for individual styles or entire outfits. The proposed system can provide higher practicality and easy-to-use interface, as it makes real-time processing possible in various digital environment, and creates comparatively natural and realistic virtual wearing styles, and also makes semi -automatic processing possible to reduce the manual works to a minimum. According to the proposed system, it can motivate the creative activity of the designers with simulation results on the effect of textile pattern design on the appearance of clothes without manufacturing physical clothes and, as it can help the purchasers for decision-making with them, promote B2B or B2C e-commerce.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.90-101
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• 2012

The landmark selection is crucial to successful perform in SLAM(Simultaneous Localization and Mapping) with a mono camera. Especially, in unknown environment, automatic landmark selection is needed since there is no advance information about landmark. In this paper, proposed visual attention system which modeled human's vision system will be used in order to select landmark automatically. The edge feature is one of the most important element for attention in previous visual attention system. However, when the edge feature is used in complicated indoor area, the response of complicated area disappears, and between flat surfaces are getting higher. Also, computation cost increases occurs due to the growth of the dimensionality since it uses the responses for 4 directions. This paper suggests to use a corner feature in order to solve or prevent the problems mentioned above. Using a corner feature can also increase the accuracy of data association by concentrating on area which is more complicated and informative in indoor environments. Finally, this paper will prove that visual attention system based on corner feature can be more effective in SLAM compared to previous method by experiment.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.267-276
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• 2003

Many countries have established eBook standards adequate to their environments. In USA, OEB PS is announced for distribution and display of eBooks, in Japan, JepaX is announced for storage and exchange, and in Korea, EBKS is made for clear exchange of eBook contents. These diverse objectives lead to different content structures. These variety of content structure will cause a problem in exchanging them. To correctly exchange eBook contents, the content structure should be considered. So, In this paper, we study conversion methods of standard eBooks contents based on Korean eBook standard, with contemplating content structure. To convert contents properly, the mapping relations should be clearly defined. For this, we consider standard's structure and extension mechanisms, and use path notations and namespaces for precise description. Moreover, through analysis of each mapping relationships, we classify conversion cases into automatic, semi-automatic, and manual conversions. Finally we write up conversion scripts and experiment with them.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1860-1871
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• 2011

Presently, The Regular Block System(Automatic, Interlocking block system) is usually used during the operation of block section. However, In case that the regular block system fails because of the failure of the fixed signals and block equipment or in case of the unexpected emergent situation which should drive on the single-track due to the accidents in the double-track section or the repair work of the one-track, the Substitute Block System to make use of the driving permission license(mapping ticket, mapping paper) is used. In case of the operation of the opposite line and the temporary one-track, the safety gets worse and the SBS may cause the fatal accidents such as a head-on & a rear-end collision. Also, the unmanned railroad stations has recently increased owing to the effective operation of the stations, for it is difficult to execute the SBS in their absence. As a result, the increase of the operation time made the train delayed. Being on the rise of these problems, in this study, we analyzed the problems and difficulties of the SBS on the single line which is lacking stability and safety and on the sections combined between maned and unmaned railroad stations. And we proposed the method to improve the existing drive permission license used for 50 years into the brand-new one with state-of-the art technology and scientific way. In the era of the 21th century, Carrying out the new SBS equipped with stability and safety, we will contribute to the effective operation of trains and the satisfaction of our customers in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.127-134
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• 2018

Electric transmission towers are facilities to transport electrical power from a plant to an electrical substation. The towers are connected using power lines that are installed with a proper sag by loosening the cable to lower the tension and to secure the sufficient clearance from the ground or nearby objects. The power line sag may extend over the tolerance due to the weather such as strong winds, temperature changes, and a heavy snowfall. Therefore the periodical mapping of the power lines is required but the poor accessibility to the power lines limit the work because most power lines are placed at the mountain area. In addition, the manual mapping of the power lines is also time-consuming either using the terrestrial surveying or the aerial surveying. Therefore we utilized multiple overlapping images acquired from a low-cost drone to automatically reconstruct the power lines in the object space. Two overlapping images are selected for epipolar image resampling, followed by the line extraction for the resampled images and the redundant images. The extracted lines from the epipolar images are matched together and reconstructed for the power lines primitive that are noisy because of the multiple line matches. They are filtered using the extracted line information from the redundant images for final power lines points. The experiment result showed that the proposed method successfully generated parabolic curves of power lines by interpolating the power lines points though the line extraction and reconstruction were not complete in some part due to the lack of the image contrast.