• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.375-381
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• 2007

A novel method to register images onto 3D data, such as 3D point cloud, 3D vectors, and 3D surfaces, is proposed. The proposed method estimates the exterior orientation of a camera with respective to the 3D data though fitting pairs of the normal vectors of two planes passing a focal point and 2D and 3D lines extracted from an image and the 3D data, respectively. The fitting condition is that the angle between each pair of the normal vectors has to be zero. This condition can be represented as a numerical formula using the inner product of the normal vectors. This paper demonstrates the proposed method can estimate the exterior orientation for the image registration as simulation tests.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.165-169
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• 2012

We observed an area of 10 $deg^2$ of the Large Magellanic Cloud using the Infrared Camera (IRC) onboard AKARI. The observations were carried out using five imaging filters (3, 7, 11, 15, and $24{\mu}m$) and the prism disperser ($2-5{\mu}m$, ${\lambda}/{\Delta}{\lambda}{\sim}20$) equipped in the IRC. This paper presents an outline of the survey project and also describes very briefly the newly compiled near- to mid-infrared point source catalog. The $10{\sigma}$ limiting magnitudes are 17.9, 13.8, 12.4, 9.9, and 8.6 mag at 3.2, 7, 11, 15 and $24{\mu}m$, respectively. The photometric accuracy is estimated to be about 0.1 mag at $3.2{\mu}m$ and 0.06 - 0.07 mag in the other bands. The position accuracy is 0.3" at 3.2, 7 and $11{\mu}m$ and 1.0" at 15 and $24{\mu}m$. The sensitivities at 3.2, 7, and $24{\mu}m$ are roughly comparable to those of the Spitzer SAGE LMC point source catalog, while the AKARI catalog provides the data at 11 and $15{\mu}m$, covering the near- to mid-infrared spectral range continuously.

• International Journal of Highway Engineering
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• v.15 no.1
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• pp.155-161
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• 2013

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

• Smart Structures and Systems
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• v.33 no.6
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• pp.399-414
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• 2024

This study discusses the long-term deformation monitoring and shape sensing of bridge girder surfaces with an automated extraction scheme for point clouds in the Region Of Interest (ROI), invariant to the position of a Light Detection And Ranging system (LiDAR). Advanced smart construction necessitates continuous monitoring of the deformation and shape of bridge girders during the construction phase. An automated scheme is proposed for reconstructing geometric model of ROI in the presence of noisy non-stationary background. The proposed scheme involves (i) denoising irrelevant background point clouds using dimensions from the design model, (ii) extracting the outer boundaries of the bridge girder by transforming and processing the point cloud data in a two-dimensional image space, (iii) extracting topology of pre-defined targets using the modified Otsu method, (iv) registering the point clouds to a common reference frame or design coordinate using extracted predefined targets placed outside ROI, and (v) defining the bounding box in the point clouds using corresponding dimensional information of the bridge girder and abutments from the design model. The surface-fitted reconstructed geometric model in the ROI is superposed consistently over a long period to monitor bridge shape and derive deflection during the construction phase, which is highly correlated. The proposed scheme of combining 2D-3D with the design model overcomes the sensitivity of 3D point cloud registration to initial match, which often leads to a local extremum.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.78.1-78.1
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• 2012

We present near-IR imaging polarimetry of the 5${\times}$9 fields (-39'${\times}$69') centered at 30 Doradus in the Large Magellanic Cloud (LMC), using the InfraRed Survey Facility (IRSF). We obtained polarimetry data in J, H, and Ks bands using the JHKs-simultaneous imaging polarimeter SIRPOL in 2008 December and 2011 December. We measured Stokes parameters of point-like sources to derive the degree of polarization and the polarization position angle. Since our results are suffered from non-photometric weather, we compare the polarization results from 2008 and those from 2011, and examine the photometric reliabilities between the two runs. Our survey data will be compared with molecular and dust maps to reveal the large-scale magnetic field properties in the star-forming clouds.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.52.2-52.2
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• 2013

We compile a near-infrared photometric and polarimetric catalog for the $5{\times}9$ fields (${\sim}39^{\prime}{\times}69^{\prime}$) in the eastern side of the Large Magellanic Cloud (LMC). The photometric and the polarimetric data were obtained in J, H, and Ks bands using JHKs-simultaneous imaging polarimeter SIRPOL of the InfraRed Survey Facility (IRSF) in 2008 December and 2011 December. We estimate quality of the data using the method and the result from the IRSF Magellanic Clouds point source catalog which was published on 2007 June. In this poster, we present configuration of the catalog and the results of the verification.

• Water for future
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• v.25 no.4
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• pp.51-59
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• 1992

Recently the pysically based precipitation model was developed by Geogakakos and Bras(1984) for the storm event. This is a modified version of the model. In a different way from the model, in this paper, it is emphasized that the hyderometeor size distribution(HSD)is subject to rainfall intensity and effects on the productivity of precipitation. The to HSD functions are applied to the equation of the outflow after mass through the cloud top and base, products of rainfall rate at the ground level, storage of cloud layer. As an input we put the meterological data observed at Chonju in Korea in our models and adjust the parameters included in it. The result show that in the model there is significant deviation between the hourly calculated rainfall rate and the observed data, while it is very small in the our model based on the two HSD.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4715-4721
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• 2022

A comprehensive approach for modeling the pulse height spectra of gamma-ray detectors from passing radioactive cloud in a case of accident at NPP has been developed. It involves modeling the transport of radionuclides in the atmosphere using Lagrangian stochastic model, WRF meteorological processor with an ARW core and GFS data to obtain spatial distribution of radionuclides in the air at a given moment of time. Applying representation of the cloud as superposition of elementary sources of gamma radiation the pulse height spectra are calculated based on data on flux density from point isotropic sources and detector response function. The proposed approach allows us to obtain time-dependent spectra for any complex radionuclide composition of the release. The results of modeling the pulse height spectra of the scintillator detector NaI(Tl) Ø63×63 mm for a hypothetical severe accident at a NPP are presented.