• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.746-754
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• 2008

A 3D stereoscopic image is generated by interdigitating every scene with video editing tools that are rendered by two cameras' views in 3D modeling tools, like Autodesk MAX(R) and Autodesk MAYA(R). However, the depth of object from a static scene and the continuous stereo effect in the view of transformation, are not represented in a natural method. This is because after choosing the settings of arbitrary angle of convergence and the distance between the modeling and those two cameras, the user needs to render the view from both cameras. So, the user needs a process of controlling the camera's interval and rendering repetitively, which takes too much time. Therefore, in this paper, we will propose the 3D stereoscopic image editing system for solving such problems as well as exposing the system's inherent limitations. We can generate the view of two cameras and can confirm the stereo effect in real-time on 3D modeling tools. Then, we can intuitively determine immersion of 3D stereoscopic image in real-time, by using the 3D stereoscopic image preview function.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1003-1004
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• 2008

In this paper, we propose a 3D modeling method using Laser Slit Beam and Stereo Camera. We can get depth information of image by analyzing projected Laser Slit Beam on object. 3D modeling is demanded exquisite merge of 3D data. In our approach, we can get the depth image where the reliability is high. Each reconstructed 3D modeling is combined by the sink information which is acquired by SIFT (Scale Invariant Feature Transform) Algorithm. We perform experiments using indoor images. The results show that the proposed method works well in indoor environments

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.848-853
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• 2005

3D geometric modeling of an object of interest has been intensively investigated in many fields including CAD/CAM and computer graphics. Traditionally, CAD and geometric modeling tools are widely used to create geometric models that have nearly the same shape of 3D real objects or satisfy designers intent. Recently, with the help of the reverse engineering (RE) technology, we can easily acquire 3D point data from the objects and create 3D geometric models that perfectly fit the scanned data more easily and fast. In this paper, we present 3D geometric model generation based on a stereo vision system (SVS) using random pattern projection. A triangular mesh is considered as the resulting geometric model. In order to obtain reasonable results with the SVS-based geometric model generation, we deal with many steps including camera calibration, stereo matching, scanning from multiple views, noise handling, registration, and triangular mesh generation. To acquire reliable stere matching, we project random patterns onto the object. With experiments using various random patterns, we propose several tips helpful for the quality of the results. Some examples are given to show their usefulness.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.393-400
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• 2017

Generally, in special effect video and 3D animation design process, character and background's 3D model is built by 3D program like MAYA or 3DS MAX. But in that manual modeling mode, model design needs much time and costs much money. In this paper, two experimental groups are set to prove use 2D photo scan modeling mode to build 3D model is effective and advanced. The first experimental group is modeling the same object by different experimental setting. The second experimental group is modeling the same background by different experimental setting. Through those two experimental groups, we try to find an effective design method and matters need attention when we use photo scan design mode. We aim to get the model from whole experiment and prove photo scan modeling mode is effective and advanced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.6
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• pp.687-695
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• 2011

Many internet portals provide maps with spatial information services. Recently, various images including aerial, satellite, street view, and photo-realistic 3D city models are provided as well as maps. This study suggested a method for geometric correction of the panoramic aerial images in the internet portal and 3D building modeling using information which is available in the internet. The key of this study is to obtain all necessary data easily from internet without restrictions. Practically, the ground control coordinates could be available from geo-referenced internet maps, and stereo pairs of the aerial images and close-range photographs for photo-realistic object modeling are provided by the internet service. However, the ground control points are not suitable for accurate mapping. RMSE of the plotting was about 9 meters and reduced upto 4 meters after coordinate transformation. The proposed methods would be applicable to various applications of photo-realistic object modeling which do not require high accuracy.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.26-34
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• 1999

This study shows an alternative method for the determination of object's position, based on a computer vision method. This approach develops the vision system model to define the reciprocal relationship between the 3-D real space and 2-D image plane. The developed model involves the bilinear six-view parameters, which is estimated using the relationship between the camera space location and real coordinates of known position. Based on estimated parameters in independent cameras, the position of unknown object is accomplished using a sequential estimation scheme that permits data of unknown points in each of the 2-D image plane of cameras. This vision control methods the robust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the robot, and correct knowledge of the relative positions and orientation of the robot and CCD camera. Finally, the developed vision control method is tested experimentally by performing determination of object position in the space using computer vision system. These results show the presented method is precise and compatible.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.347-356
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• 2009

Recently many kind of industry request 3D Modeling and visualize for object. So application of 3D laser scanning system is improved for 3D modeling and survey. But although application of scanner is steady improved, there are variety of caution error is contained in the result. There is request high quality survey for it is restricted by when field. To analysis what is consider major factor of reflect ratio about surface specification as ratio and characteristics accidental error. So this research use 3D laer scanning system what is adopted method of laser measurement for improvement accuracy 3D scan data. As a result we can check reflect volume will be improve as specification of survey object if it has high reflect ratio. It able to improve accuracy how laser scan data is revised by reflect volume.

• Investigative Magnetic Resonance Imaging
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• v.22 no.3
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• pp.141-149
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• 2018

Purpose: The purpose of this study is to develop a simple method to measure magnetic susceptibility of arbitrarily shaped materials through MR imaging and numerical modeling. Materials and Methods: Our 3D printed phantom consists of a lower compartment filled with a gel (gel part) and an upper compartment for placing a susceptibility object (object part). The $B_0$ maps of the gel with and without the object were reconstructed from phase images obtained in a 3T MRI scanner. Then, their difference was compared with a numerically modeled $B_0$ map based on the geometry of the object, obtained by a separate MRI scan of the object possibly immersed in an MR-visible liquid. The susceptibility of the object was determined by a least-squares fit. Results: A total of 18 solid and liquid samples were tested, with measured susceptibility values in the range of -12.6 to 28.28 ppm. To confirm accuracy of the method, independently obtained reference values were compared with measured susceptibility when possible. The comparison revealed that our method can determine susceptibility within approximately 5%, likely limited by the object shape modeling error. Conclusion: The proposed gel-phantom-based susceptibility measurement may be used to effectively measure magnetic susceptibility of MR-compatible samples with an arbitrary shape, and can enable development of various MR engineering parts as well as test biological tissue specimens.