• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.29-34
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• 2002

In this paper, we present a new geometric active contour model based on level set methods introduced by Osher and Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image. Using anisotropic diffusion filtering for each slice, we have the result with reduced noise and extracted exact shape. Volume rendering operates on three-dimensional data, processes it, and transforms it into a simple two-dimensional image.

• Progress in Medical Physics
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• v.32 no.4
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.163-180
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• 2020

In this study, to build a spatial information infrastructure, which is a component of a smart city, a 3D digital twin model in the downtown area was built based on the latest spatial information acquisition technology, the drone. Several analysis models were implemented by utilizing. While the data processing time and quality of the three types of drone photogrammetry software are different, the accuracy of the construction model is ± 0.04 in the N direction and ± 0.03m in the E direction. In the m and Z directions, ± 0.02m was found to be less than 0.1m, which is defined as the allowable range of surveying performance and inspection performance for the boundary point in the area where the registration of the boundary point registration is executed. 1: 500 to 1 of the aerial survey work regulation: The standard deviation, which is the error limit of the photographic reference point of the 600 scale, appeared within 0.14 cm, and it was found that the error limit of the large scale specified in the cadastral and aerial survey was satisfied. In addition, in order to increase the usability of smart city realization using a drone-based 3D urban digital twin model, the model built in this study was used to implement Prospect right analysis, landscape analysis, Right of light analysis, patrol route analysis, and fire suppression simulation training. Compared to the existing aerial photographic survey method, it was judged that the accuracy of the naked eye reading point is more accurate (about 10cm) than the existing aerial photographic survey, and it is possible to reduce the construction cost compared to the existing aerial photographic survey at a construction area of about 30㎢ or less.

• Spatial Information Research
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• v.17 no.4
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• pp.469-481
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• 2009

Due to the increasing demands on the efficient use of land and the fast growth of construction technologies, human living space is expanded from on the surface to above and under the surface. By recognizing that the current cadastre system based on 2D was not appropriate to reflect the trend, the researchers are interested in a 3D cadastre. This paper proposed the 3D cadastre data model that is appropriate to protect ownership effectively in Korea. The 3D cadastre data model consists of a 3D cadastre feature model and a 3D cadastre geometry model, and the data are produced by a 3D cadastre data structure. A 3D cadastre feature model is based on 3D rights and features derived from case studies. A 3D cadastre geometry model based on ISO19107 Spatial Schema is modified to be good for 3D cadastre in Korea. A 3D cadastre data structure consists of point, line, polygon and solid primitives. This study finally purposes 1) serving and managing land information effectively, 2) creating rights and displaying ranges about infrastructures above and under surface, 3) serving ubiquitous-based geoinformation, 4) adapting ubiquitous-based GIS to urban development, and 5) regulating relationships between rights of land and registration and management systems.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.53-61
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• 2006

In this paper, we propose a novel method for 3D reconstruction of an indoor scene using a multi-view camera. Until now, numerous disparity estimation algorithms have been developed with their own pros and cons. Thus, we may be given various sorts of depth images. In this paper, we deal with the generation of a 3D surface using several 3D point clouds acquired from a generic multi-view camera. Firstly, a 3D point cloud is estimated based on spatio-temporal property of several 3D point clouds. Secondly, the evaluated 3D point clouds, acquired from two viewpoints, are projected onto the same image plane to find correspondences, and registration is conducted through minimizing errors. Finally, a surface is created by fine-tuning 3D coordinates of point clouds, acquired from several viewpoints. The proposed method reduces the computational complexity by searching for corresponding points in 2D image plane, and is carried out effectively even if the precision of 3D point cloud is relatively low by exploiting the correlation with the neighborhood. Furthermore, it is possible to reconstruct an indoor environment by depth and color images on several position by using the multi-view camera. The reconstructed model can be adopted for interaction with as well as navigation in a virtual environment, and Mediated Reality (MR) applications.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.775-778
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• 2008

Augmented Reality is a growing area in virtual reality research, The world environment around us provides a wealth of information that is difficult to duplicate in a computer. This evidenced by the worlds used in virtual environments. An augmented reality system generates a composite view for the user. It is a combination of the real scene viewed by the user and a virtual scene generated by the computer that augments the scene with addition information. The registration method represent to the user enhances that person's performance in and perception of the world. It decide the direction and location between real world and 3D graphic objects. The registration method devide two method, Model based tracking and Move-Matching. This paper researched at to generate a commerce correlation using a tracking object method, using at a color distribution and information, in the sequence scene.

• Journal of the Korea Institute of Building Construction
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• v.20 no.3
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• pp.289-296
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• 2020

This study suggests a methodology for automatically extracting placing information from 2D reinforced concrete details drawings and generating a 3D reinforcement placing model to develop a mobile augmented reality for bar placing work. To make it easier for users to acquire placing information, it is suggested that users takes pictures of structural drawings using a camera built into a mobile device and extract placing information using vision recognition and the OCR(Optical Character Registration) tool. In addition, an augmented reality app is implemented using the game engine to allow users to automatically generate 3D reinforcement placing model and review the 3D models by superimposing them with real images. Details are described for application to the proposed methodology using the previously developed programming tools, and the results of implementing reinforcement augmented reality models for typical members at construction sites are reviewed. It is expected that the methodology presented as a result of application can be used for learning bar placing work or construction review.

• Spatial Information Research
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• v.21 no.3
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• pp.55-69
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• 2013

In the modern society, as the city grows and constructive technology gradually develops, land usage has been sophisticated and three-dimensionalized. Consequently, issues such as property ownership and legal claim over ground and underground space have been triggered, which disclose the problems of using two-dimensional cadastral management system. Numerous researches on building three-dimensional cadastral are ongoing such as defining the right relationship of land space and providing the registration and management scheme so as to solve the imminent matter. It is the chief aim of this study to increase efficiency by analyzing the vocational practices through adding on necessary sections and properties for current registration to original research work. If three-dimensional cadastral management system is once constructed, highly qualified services for citizens will be available by providing accurate land related information swiftly, which can result in inevitable improvement of efficiency at work. Hence, this thesis will suggest the internationally suitable feature model development method in terms of standardization by probing into the factual profession to derive each attributes and properties that are related to three-dimensional cadastral and affix them to the classified item requisites of initial research studies.

• Journal of Korean Dental Science
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• v.15 no.1
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• pp.61-67
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• 2022

Purpose: This study aimed to evaluate the accuracy of bite registration using intraoral scanner based on data trimming strategy for fremitus teeth. Materials and Methods: A reference model was designed by Medit Model Builder software (MEDIT Corp., Seoul). Tooth number 24 and 25 were separated as dies and tooth number 26 was prepared for full-coverage crown. Those were printed using a 3D printer (NextDent 5100). The scanning procedure was performed by a single trained operator with one intraoral scanner (i700; MEDIT Corp.). The scanning groups were divided as follows: group 1 (G1), no fremitus; group 2 (G2), 0.5 mm buccal fremitus in the maxillary left first and second premolar; and group 3 (G3), 1.5 mm buccal fremitus in the maxillary left first and second premolar. Each group was scanned 10 times and were analyzed using the reference model data. Surface-based occlusal clearance was analyzed at the prepared tooth to evaluate accuracy. Result: Mean values of control group (G1) were 1.587±0.021 mm. G2 showed similar values to those from the control group (1.580±0.024 mm before trimming strategy and 1.588±0.052 mm after trimming strategy). G3 showed significantly greater values (1.627±0.025 mm before trimming strategy and 1.590±0.024 mm after trimming strategy) and the differences were found between trimming strategy (P=0.004). Conclusion: Bite trimming strategy for fremitus teeth is a reliable technique to reduce inaccuracies caused by the mobility at maximum intercuspation.

• 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.