• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2782-2805
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• 2018

We present a legorization framework that produces a LEGO model from user-specified 3D mesh model. Our framework is composed of two stages: voxelization and legorization. In the voxelization, input 3D mesh is converted to a voxel model. To preserve the shape of the 3D mesh, we devise a silhouette fitting process for the initial voxel model. For legorization, we propose three objectives: stability, aesthetics and efficiency. These objectives are expressed in a tiling equation, which builds a LEGO model using layer-by-layer approach. We legorize five models including characters and buildings to prove the excellence of our framework.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Korean Journal of Computational Design and Engineering
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• v.11 no.4
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• pp.265-272
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• 2006

For the design and analysis of 3D object featuring complexity and irregularity in shape, sectional digital images measured by an industrial CT scanner are employed to generate a finite element model with uniform voxels. The voxel model plays a key role in developing an integrated reverse engineering system including geometric modeling, simulation and optimization. Design examples applied to topology optimization show that the proposed approach can provide a remarkable reduction in time cost at the conceptual and detail design stages.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.8
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• pp.91-97
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• 2010

Micro-abrasive Jet Machining is one of the new technology which enables micro-scale machining on the surface of high brittle materials. In this technology it is very important to fabricate a mask that prevents excessive abrasives not to machine un-intend surface. Our previous work introduced the micro-stereolithography technology for the mask fabrication. And is good to not only planar material but also for non-planar materials. But the technology requires a 3 dimensional mask CAD model which is perfectly matched with the surface topology of parent material as an input. Therefore there is strong need to develop an automated modeling technology which produce adequate 3D mask CAD model in fast and simple way. This paper introduces a fast and simple mask modeling algorithm which represents geometry of models in voxel. Input of the modeling system is 2D pattern image, 3D CAD model of parent material and machining parameters for Micro-abrasive Jet Machining. And the output is CAD model of 3D mask which reflects machining parameters and geometry of the parent material. Finally the suggested algorithm is implemented as software and verified by some test cases.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.471-476
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• 2022

In this paper, we propose an object detection using a 4D imaging radar, which developed to solve the problems of weak cameras and LiDAR in bad weather. When data are measured and collected through a 4D imaging radar, the density of point cloud data is low compared to LiDAR data. A technique for clustering objects and extracting the features of objects through voxels in the cluster is proposed using the characteristics of wide distances between objects due to low density. Furthermore, we propose an object detection using the extracted features.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.544-555
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• 2002

The aim of this study is to develop the $^1H$-MRS data postprocessing software for both single-voxel and multi-voxel technique, which plays and important role as a diagnostic tool in clinical field. This software is based on graphical user interface(GUI) under windows operating system of personal computer(PC). In case of single-voxel MRS, both of raw data in time-domain and spectrum data in frequency-domain are simultaneously displayed in a screen. Several functions such as DC correction, zero filling, line broadening, Lorentz-Gauss filtering and phase correction, etc. are included to increase the quality of spectrum data. In case of multi-voxel analysis, spectroscopic image reconstructed by 3-D FFT was displayed as a spectral grid and overlapped over previously obtained T1- or T2-weighted image for the spectra to be spatially registered with the image. The analysis of MRS peaks were performed by obtaining the ratio of peak area. In single-voxel method, statistically processed peak-area ratios of MRS data obtained from normal human brain are presented. Using multi-voxel method, MR spectroscopic image and metabolite image acquired from brain tumor are demonstrated.

• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.116-122
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• 2017

Physical breast phantoms would be useful for the development of a dedicated breast computed tomography (BCT) system and its optimization. While the conventional breast phantoms are available in compressed forms, which are appropriate for the mammography and digital tomosynthesis, however, the BCT requires phantoms in uncompressed forms. Although simple cylindrical plastic phantoms can be used for the development of the BCT system, they will not replace the roles of uncompressed phantoms describing breast anatomies for a better study of the BCT. In this study, we have designed a numerical voxel breast phantom accounting for the random nature of breast anatomies and applied it to the 3D printer to fabricate the uncompressed anthropomorphic breast phantom. The numerical voxel phantom mainly consists of the external skin and internal anatomies, including the ductal networks, the glandular tissues, the Cooper's ligaments, and the adipose tissues. The voxel phantom is then converted into a surface data in the STL file format by using the marching cube algorithm. Using the STL file, we obtain the skin and the glandular tissue from the 3D printer, and then assemble them. The uncompressed breast phantom is completed by filling the remaining space with oil, which mimics the adipose tissues. Since the breast phantom developed in this study is completely software-generated, we can create readily anthropomorphic phantoms accounting for diverse human breast anatomies.

• 한국공간정보시스템학회:학술대회논문집
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• 2003.11a
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• pp.159-162
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• 2003

In this paper, to find probable collision positions between moving object and terrain in 3D space efficiently, we use a model, similar to Ray Tracing, which finds the triangles intersected by a directed line segment from a large amount of triangles. We try to reduce dead space as much as possible to find candidate triangles intersected by a directed line segment than previous work's. A new modified octree, LBV-Octree(Least Bounding Voxel Octree), is proposed, and we have a ray tracing with it. In the experiment, ray tracing with LBV-Octree provides $5%{\sim}11%$ better performance than with classical octree.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.571-581
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• 2020

Vegetation affects water level change and flow resistance in rivers and impacts waterway ecosystems as a whole. Therefore, it is important to have accurate information about the species, shape, and size of any river vegetation. However, it is not easy to collect full vegetation data on-site, so recent studies have attempted to obtain large amounts of vegetation data using terrestrial laser scanning (TLS). Also, due to the complex shape of vegetation, it is not easy to obtain accurate information about the canopy area, and there are limitations due to a complex range of variables. Therefore, the physical structure of vegetation was analyzed in this study by reconfiguring high-resolution point cloud data collected through 3-dimensional terrestrial laser scanning (3D TLS) in a voxel. Each physical structure was analyzed under three different conditions: a simple vegetation formation without leaves, a complete formation with leaves, and a patch-scale vegetation formation. In the raw data, the outlier and unnecessary data were filtered and removed by Statistical Outlier Removal (SOR), resulting in 17%, 26%, and 25% of data being removed, respectively. Also, vegetation volume by voxel size was reconfigured from post-processed point clouds and compared with vegetation volume; the analysis showed that the margin of error was 8%, 25%, and 63% for each condition, respectively. The larger the size of the target sample, the larger the error. The vegetation surface looked visually similar when resizing the voxel; however, the volume of the entire vegetation was susceptible to error.