• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.243-260
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• 2000

Ocean remote sensing reflectance of just above water level was modeled using inherent optical properties of seawater contents, total absorption (a) and backscattering(bb) coefficients ($R_{rs}$=0.046 $b_b$/(a+$b_b$). This modeling was based on the specific absorption and backscattering coefficients of 5 optically active seawater components； phytoplankton pigments, non-chlorophyllous suspended particles, dissolved organic matters, heterotrophic microorganisms, and the other unknown particle components. Simulated remote sensing reflectance($R_{rs}$) and water leaving radiance(Lw) spectra were well agreed with in-situ measurements obtained using a bi-directional fields remote spectrometer in coastal waters and open ocean. $R_{rs}$ values in SeaWiFS bands from the model were analyzed to develop 2-band ratio ocean color chlorophyll with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The model algorithms were examined and compared with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The remote reflectance model will be very helpful to understand the variation of water leaving radiances caused by the various components in the seawater, and to develop new ocean color algorithm for CASE-II water using neural network method or other analytical method, and in the model of fine atmospheric signal correction.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.600-602
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• 2022

2차원 이미지 또는 영상을 통한 자세 추정의 경우, 영상 내에서 발생할 수 있는 탐지 오류, 피사체 잘림, 폐색(Occlusion) 등으로 인해 자세 추정 정확도가 감소할 수 있다. 본 논문에서는 4장 이상의 다양한 각도로 촬영한 이미지를 NeRF(Neural Radiance Fields)를 통해 이미지 합성(Image synthesis)을 진행하여 3차원 모델을 생성한다. 이후 DeepLabCut을 사용하여 관절 좌표와 골격(Skeleton)을 구축한다. 구축한 골격을 인공지능에 학습시킨 뒤 2차원 영상에서의 관절 좌표 인식, 골격 구축, 자세 추정을 진행한다. 2차원 영상 테스트 데이터를 통해, 3차원 모델을 사전 학습한 인공지능 모델과 기존 2차원 이미지를 사용하여 학습한 인공지능 모델의 자세 추정 정확도를 비교한다.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.83-90
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• 2024

CNC tools contribute to the production of high-precision and consistent results. However, employing damaged CNC tools or utilizing compromised numerical control can lead to significant issues, including equipment damage, overheating, and system-wide errors. Typically, the assessment of external damage to CNC tools involves capturing a single viewpoint through a camera to evaluate tool wear. This study aims to enhance existing methods by using only a single manually focused Microscope camera to enable comprehensive external analysis from multiple perspectives. Applying the NeRF (Neural Radiance Fields) algorithm to images captured with a single manual focus microscope camera, we construct a 3D rendering system. Through this system, it is possible to generate scenes of areas that cannot be captured even with a fixed camera setup, thereby assisting in the analysis of exterior features. However, the NeRF model requires considerable training time, ranging from several hours to over two days. To overcome these limitations of NeRF, various subsequent models have been developed. Therefore, this study aims to compare and apply the performance of Instant NGP, Mip-NeRF, and DS-NeRF, which have garnered attention following NeRF.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.98-105
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• 2024

This paper proposes a new approach with a distance-based regularization to the entropy applied to the NBV (Next-Best-View) selection with NeRF (Neural Radiance Fields). 3D reconstruction requires images from various viewpoints, and selecting where to capture these images is a highly complex problem. In a recent work, image acquisition was derived using NeRF's ray-based uncertainty. While this work was effective for evaluating candidate viewpoints at fixed distances from a camera to an object, it is limited when dealing with a range of candidate viewpoints at various distances, because it tends to favor selecting viewpoints at closer distances. Acquiring images from nearby viewpoints is beneficial for capturing surface details. However, with the limited number of images, its image selection is less overlapped and less frequently observed, so its reconstructed result is sensitive to noise and contains undesired artifacts. We propose a method that incorporates distance-based regularization into entropy, allowing us to acquire images at distances conducive to capturing both surface details without undesired noise and artifacts. Our experiments with synthetic images demonstrated that NeRF models with the proposed distance and entropy-based criteria achieved around 50 percent fewer reconstruction errors than the recent work.