• Journal of the Korean Society for Precision Engineering
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• v.23 no.2 s.179
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• pp.164-171
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• 2006

Anterior cruciate ligament(ACL) injury of the knee is common and a serious ACL injury leads to ligament reconstruction surgery. Gait analysis is used to identify the result of surgery. The purpose of this study is to numerically evaluate and classify knee condition of patients through the chaos analysis. Experiments were carried out for 13 subjects (8 healthy subjects, 5 ACL deficient patients) walking on a treadmill. Sagittal kinematic data of the right lower extremity were collected by using a 3D motion analysis system. The recorded gait patterns were digitized and then coordinated by KWON3D. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. It was found that the Lyapunov exponent becomes larger as the knee condition becomes worse. This study suggested a method of the severity of injury and the level of recovery. The proposed method discerns difference between healthy subjects and patients.

• Korean Journal of Head & Neck Oncology
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• v.26 no.2
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• pp.193-197
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• 2010

Objectives : Patients with neck tumor and their family need every information about the disease. Especially, the size and location are confusing with verbal information. With the aid of CT, the problem had some answer, but it needs some medical education. We would like to know the usefullness of 3D reconstructed images in patient education about the disease. Material and Methods : Neck CT data were collected from 10 patients with various neck tumors and converted to 3D reconstructed images. Understanding of the patients about the size and location of tumors were rated from questionaires using axial CT images and 3D images. Results : Understanding score about 3D images were greater than that of CT images(p<0.006). Conclusion : 3D reconstructed images of CT could give the patients more real visual information about the disease.

• 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.545-555
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• 2007

3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.

• Journal of IKEEE
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• v.16 no.2
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• pp.95-101
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• 2012

A novel approach for the reconstruction of 3D building model from aerial image data is proposed in this paper. In this approach, a Centroid Neural Network (CNN) with a metric of line segments is proposed for connecting low-level linear structures. After the straight lines are extracted from an edge image using the CNN, rectangular boundaries are then found by using an edge-based grouping approach. In order to avoid producing unrealistic building models from grouping lined segments, a hierarchical grouping method is proposed in this paper. The proposed hierarchical grouping method is evaluated with a set of aerial image data in the experiment. The results show that the proposed method can be successfully applied for the reconstruction of 3D building model from satellite images.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.789-799
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• 2008

For the communication between human and computer in an interactive computing environment, the gesture recognition has been studied vigorously. The algorithms which use the 2D features for the feature extraction and the feature comparison are faster, but there are some environmental limitations for the accurate recognition. The algorithms which use the 2.5D features provide higher accuracy than 2D features, but these are influenced by rotation of objects. And the algorithms which use the 3D features are slow for the recognition, because these algorithms need the 3d object reconstruction as the preprocessing for the feature extraction. In this paper, we propose a method to extract the 3D features combined with the 3D object reconstruction in real-time. This method generates three kinds of 3D projection maps using the modified GPU-based visual hull generation algorithm. This process only executes data generation parts only for the gesture recognition and calculates the Hu-moment which is corresponding to each projection map. In the section of experimental results, we compare the computational time of the proposed method with the previous methods. And the result shows that the proposed method can apply to real time gesture recognition environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.11-16
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• 2023

The technology of 3D reconstruction, primarily relying on point cloud data, is essential for digitizing objects or spaces. This paper aims to utilize reinforcement learning to achieve the acquisition of point clouds in a given environment. To accomplish this, a simulation environment is constructed using Unity, and reinforcement learning is implemented using the Unity package known as ML-Agents. The process of point cloud acquisition involves initially setting a goal and calculating a traversable path around the goal. The traversal path is segmented at regular intervals, with rewards assigned at each step. To prevent the agent from deviating from the path, rewards are increased. Additionally, rewards are granted each time the agent fixates on the goal during traversal, facilitating the learning of optimal points for point cloud acquisition at each traversal step. Experimental results demonstrate that despite the variability in traversal paths, the approach enables the acquisition of relatively accurate point clouds.

• Journal of the Korea Computer Graphics Society
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• v.2 no.1
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• pp.68-76
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• 1996

One of the major problems in GIS(Geographical Information Systems) involves acquiring 3-D terrain data. Because conventional methods such as land surveying or analysis of aerial photographs are costly, the method of using existing paper maps has been gaining considerable attention. This method demands three processing steps: 1) extraction of contours, 2) assignment of height values to the extracted contours, 3) reconstruction of 3-D terrain data. In this paper we systematically develop a procedure for acquiring 3-D terrain data from contour solutions. For the first two steps, we describe the necessary operations and roughly sketch solutions. For the last step, we propose an efficient raster-based algorithm and present the results of experiments with existing paper map images.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.537-543
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• 2004

In order to create three-dimensional model for human body, a method that reconstructs geometric models from contour lines on cross-section images is commonly used. We can get a set of contour lines by acquiring CT or MR images and segmenting anatomical structures. Previously proposed method divides entire contour line into simply matched regions and clefts. Since long processing time is required for reconstructing cleft regions, its performance might be degraded when manipulating complex data such as cross-sections for human body. In this paper, we propose a fast reconstruction method. It generates a triangle strip with single tiling operation for simple region that does not contain branch structures. If there exist branches in contour lines, it partitions the contour line into several sub-contours by considering the number of vertices and their spatial distribution. We implemented an automatic surface reconstruction system by using our method which reconstructs three-dimensional models for anatomical structures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.187-196
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• 1994

This study deals with roughing planning by cross sectional information generated from sculptured surfaces. Bicubic Bezier surface is adopted as sculptured surfaces in this paper. The system consists of 3 pstyd : 1) modeling sculptured surface, 2) reconstruction of cross-section in 2D coordinates, 3) determination of roughing tool path with structural data. The system is developed by using BIM-PC in the environment of Auto CAD R11, AutoLISP and MetaWare C. The proposed system shows an efficient algorithm for roughing planning with cross sectional information.