• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.507-516
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• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• Journal of radiological science and technology
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• v.43 no.5
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• pp.397-404
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• 2020

Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.383-390
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• 2020

Car-mounted MMS (Mobile Mapping System) is the most effective tool for mapping of high definition road maps(HD Map). The MMS is composed of various sensor combinations, and the manufacturing methods and processing software are different for each manufacturer, performance cannot be predicted only by the specifications of the parts. Therefore, it is necessary to judge whether each equipment is suitable for mapping through performance evaluation, and facilities for periodic performance evaluation. In this paper, we explained the MMS performance evaluation facilities built at the SOC Evaluation Research Center of Korea Institute of Civil Engineering and Building Technology and analyzed the conditions that the evaluation facilities should have through a literature survey and field tests.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.70-77
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• 2017

Among available people counting methods, map-based approaches based on moving interest points have shown good performance. However, the stationary people counting is challenging in such methods since all static points of interest are considered as background. To include stationary people in counting, it is needed to discriminate between the static points of stationary people and the background region. In this paper, we propose a people counting method based on using both moving and static points. The proposed method separates the moving and static points by motion information. Then, the static points of the stationary people are classified using foreground mask processing and point pattern analysis. The experimental results reveal that the proposed method provides more accurate count estimation by including stationary people. Also, the background updating is enabled to solve the static point misclassification problem due to background changes.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.366-374
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• 2017

Since a display device such as TV or digital signage is getting larger, the types of media is getting changed into wider view one such as UHD, panoramic and jigsaw-like media. Especially, panoramic and jigsaw-like media is realized by stitching video clips, which are captured by different camera or devices. However, a stitching process takes long time, and has difficulties in applying for a real-time process. Thus, this paper suggests to find out 2D Adjacency Matrix, which tells spatial relationships among those video clips in order to decrease a stitching processing time. Using the Discrete Cosine Transform (DCT), we convert the each frame of video source from the spatial domain (2D) into frequency domain. Based on the aforementioned features, 2D Adjacency Matrix of images could be found that we can efficiently make the spatial map of the images by using DCT. This paper proposes a new method of generating 2D adjacency matrix by using DCT for producing a panoramic and jigsaw-like media through various individual video clips.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.217-225
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• 2017

A study proposes possibility of new IoT measuring system blended with a smart device. The research serves the best cultivation information for domestic fruit's enhancement of competitive power and also develops a glucose measuring system by which people manage fructose with the mobile device. The mobile glucose tester is designed with a form of accessory which has high portability and utility because the product connects an existing analogy refractometer to the smart phone. You can check the glucose rates data by commodity, region, and season then save measurement results with server in real time for an exclusive application. It's possible to serve the glucose map, graph, and data list through the web service. This is very useful to do collect, analyze, and process the glucose data.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.60-67
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• 2008

An all-optical 4-bit Gray code to binary coded decimal (BCD) converter by means of commercially available numerical analysis tool (VPI) was demonstrated, for the first time to our knowledge. Circuit design approach was modified appropriately in order to fit the electrical method on an all-optical logic circuit based on a cross gain modulation (XGM) process so that signal degradation due to the non-ideal optical logic gates can be minimized. Without regenerations, Q-factor of around 4 was obtained for the most severely degraded output bit (least significant bit-LSB) with 2.5 Gbps clean input signals having 20 dB extinction ratio. While modifying the two-level simplification method and Karnaugh map method to design a Gray code to BCD converter, a general design concept was also founded (one-level simplification) in this research, not only for the Gray code to BCD converter but also for any general applications.

• Asian Journal of Atmospheric Environment
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• v.7 no.1
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• pp.25-37
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• 2013

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.58-65
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• 2002

The most widespread techniques for DEM generation are stereoscopy for optical sensor images and interfereometry for SAR images. These techniques suffer from certain sensor and processing limitations, which can be overcome by the synergetic use of both sensors and DEMs respectively. In this paper, different strategies for fusing SAR and optical data are combined to derive high quality DEM products. The multiresolution wavelet transform, which take advantage of the complementary properties of SAR and stereo optical DEMs, will be applied for the fusion process. By taking advantage of the fact that errors of the DEMs are of different nature using the multiresolution wavelet transform, affected part are filtered and replaced by those of the counterpart and is tested with two sets of SPOT and ERS DEM, resulting in a remarkable improvement in DEM. For the analysis of results, the reference DEM is generated from digital base map(1:5000).

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.75-83
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• 2009

This paper presents a new mobile robot localization method for indoor robot navigation. The method uses color-coded landmarks on the ceiling and a camera is installed on the robot facing the ceiling. The proposed "cell-coded map", with the use of only nine different kinds of color-coded landmarks distributed in a particular way, helps reduce the complexity of the landmark structure. This technique is applicable for navigation in an unlimited size of indoor space. The structure of the landmarks and the recognition method are introduced. And 2 rigid rules are also used to ensure the correctness of the recognition. Experimental results prove that the method is useful.