• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.501-503
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• 1999

네트워크 상에서 서로 다르게 저작된 애니메이션 기술과 데이터를 결합하여 동일한 뷰를 표현 가능하게 하기 위해선 공유 가상 환경 시스템과 같은 요소가 필요하게 된다. 이러한 시스템을 구현하기 위해선 여러 가지 요구 사항이 필요하게 된다. 즉, 데이터 공유, 멀티미디어 렌더링, 상호작용 등이다. 본 논문에서는 이러한 요구 사항을 만족하고, 서로 다른 네트워크 상에 있는 3차원 데이터를 웹을 통하여 동일된 가상 환경상에 표현함으로써 데이터 공유에 따른 애니메이션 및 물체의 제어를 효율적으로 처리 가능하게 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1629-1632
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• 2004

입체는 특수 장비에 의해 디지털 데이터화 되어 3차원 물체의 표면을 메쉬나 다각형 등으로 표현하며 입체의 세부구조인 다각형은 면적, 각 정점의 토폴로지, 변의 길이, 내각과 같은 고유 기하정보 요소를 가진다. 본 연구에서는 3차원 입체를 특징짓는 다각형의 기하학적 정보를 이용하여 3차원 모델을 2차원 평면으로 전개하는 방법을 제안한다. 이와 같은 기술은 옷이나 신발과 같은 2차원 형태의 부품을 결합하여 입체형태의 제품을 만들어 내는 분야에 매우 유용하게 쓰이며 나아가 3차원 모델에 최소의 왜곡을 가지는 텍스쳐를 적용시켜 3차원 모델의 사실성을 높이는데 기여할 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.21-24
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• 2007

3차원 컴퓨터 그래픽스에서는 3차원 모델의 표면이 유리나 거울과 같이 반사 특성을 갖는 경우, 반사 효과를 반영하여 렌더링(rendering)하게 된다. 3차원 모델에서 동영상이 물체 표면에 반사되는 경우, 반사 특성을 갖는 3차원 모델의 표면은 동영상의 변화에 따른 반사 영상이 표현되도록 해야 한다. 3차원 컴퓨터 그래픽스에서 사용하는 반사 모델을 이용하여 동영상의 반사 영상을 렌더링하게 되면 처리시간이 많이 걸리게 되므로 고성능 하드웨어의 지원이 필요하다. 본 논문에서는 3차원 컴퓨터그래픽스에서 3차원 모델에서 반사 특성을 갖는 표면에 동영상의 반사 영상을 고속으로 생성하는 방법을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.309-312
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• 2016

최근 실세계에 존재하는 물체의 3차원 형상과 색상을 디지털화하는 3차원 객체 복원에 대한 관심이 날로 증가하고 있다. 3차원 객체 복원은 영상 획득, 영상 보정, 점군 획득, 반복적 점군 정합, 무리 조정, 3차원 모델 표현과 같은 단계를 거처 통합된 3차원 모델을 생성한다. 그 중 반복적 점군 정합 방법은 카메라 궤적의 초기 값을 획득하는 방법으로서 무리 조정 단계에서 전역 최적 값으로의 수렴을 보장하기 위해 중요한 단계이다. 기존의 반복적 점군 정합 (iterative closest points) 방법에서는 시간이 지남에 따라 누적된 궤적 오차 때문에 발생하는 객체 표류 문제가 발생한다. 본 논문에서는 이 문제를 해결하기 위해 색상 영상에서 SIFT 특징점을 획득하고 3차원 점군을 얻은 뒤 가중치를 부여함으로써 점 군 간의 더 정확한 정합을 수행한다. 실험결과에서 기존의 방법과 비교하여 제안하는 방법이 절대 궤적 오차 (absolute trajectory error)가 감소하는 것을 확인 했고 복원된 3차원 모델에서 객체 표류 현상이 줄어드는 것을 확인했다.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.1-8
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• 1999

This paper presents a new algorithm for surface segmentation and feature description. In the first stage of proposed algorithm, the signature of an edge image of object is extracted. The signature technique represents a surface using the distance from the mass center to the boundary of the image as a function of angle rotating counterclockwise. If there exists a range in the angle axis where more than two signatures form a closed curve, we can conclude there is a surface inside the range. Using this feature of the signature, surface can be segmented. The surface features such as number of vertices, number of edges, convex and type of surface can also be extracted from segmented surfaces. This algorithm has distinguished advantages; it can easily recover the lost part in the edge image using the curve fitting method; it extracts surface features correctly regardless of the rotation of the surface in 3-D space.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.185-191
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• 2002

In this paper, we propose a 3D object LOD(Level of Detail) modeling system that constructs a mesh from range images and generates the mesh of various LOD using the wavelet transform. In the initial mesh generation, we use the marching cube algorithm. We modify the original algorithm to apply it to construct the mesh from multiple range images efficiently. To get the base mesh we use the decimation algorithm which simplifies a mesh with preserving the topology Finally, when reconstructing new mesh which is similar to initial mesh we calculate the wavelet coefficients by using the wavelet transform. We solve the critical problem of wavelet-based methods - the surface crease problem (1) - by using the mesh simplification as the base mesh generation method.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.200-204
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• 2008

In this paper, to solve localization problem for out-door navigation of robotic vehicles, a particle filter based 3D object recognition framework that can estimate the pose of a building or its entrance is presented. A particle filter framework of multiple evidence fusion and model matching in a sequence of images is presented for robust recognition and pose estimation of 3D objects. The proposed approach features 1) the automatic selection and collection of an optimal set of evidences 2) the derivation of multiple interpretations, as particles representing possible object poses in 3D space, and the assignment of their probabilities based on matching the object model with evidences, and 3) the particle filtering of interpretations in time with the additional evidences obtained from a sequence of images. The proposed approach has been validated by the stereo-camera based experimentation of 3D object recognition and pose estimation, where a combination of photometric and geometric features are used for evidences.

• Smart Media Journal
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• v.3 no.4
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• pp.22-27
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• 2014

Three-dimensional image reconstruction allows us to represent real objects in the virtual space and observe the objects at arbitrary view points. This technique can be used in various application areas such as education, culture, and art. In this paper, we propose an implementation method of the high-quality three-dimensional object using multiple Kinect cameras released from Microsoft. First, We acquire color and depth images from triple Kinect cameras; Kinect cameras are placed in front of the object as a convergence form. Because original depth image includes some areas where have no depth values, we employ joint bilateral filter to refine these areas. In addition to the depth image problem, there is an color mismatch problem in color images of multiview system. In order to solve it, we exploit an color correction method using three-dimensional geometry. Through the experimental results, we found that three-dimensional object which is used the proposed method is more naturally represented than the original three-dimensional object in terms of the color and shape.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.31-43
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• 2022

Recently, metaverse is rapidly expanding its area as a platform that can be applied to various fields. In particular, the function that allows many users to interact in a three-dimensional space allows VR/AR-based educational content to be used as a more advanced concept. Due to the nature of engineering education, it is often based on three-dimensional objects. In the case of a three-dimensional object, it is difficult to explain through two-dimensional videos or documents, and it becomes more difficult to express when the process of changing the object is included. The three-dimensional space of the metaverse can improve this difficulty based on real-time rendering. Another characteristic of engineering education is that there are many invisible elements. Although it is involved in the movement of objects due to electromagnetic fields, magnetic fields, and forces, it is the main reason for increasing learning difficulty because it is invisible. These problems can also help learning because they can be visually represented in the metaverse space. In this paper, the results of the establishment of the metaverse platform for engineering education and the real-time lecture contents produced based on it are described, and the applied results and lecture evaluation are discussed. Lectures using a total of 9 metaverse contents were conducted, and 90% of the positive lecture evaluation results were obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.117-127
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• 1995

In this paper, we suggest a motion analysis method using ART-I1 competitive learning neural network and fuzzy reasoning by matching the same objects through the consecutive image sequence. we use the size and mean intensity of the region obtained from image segmentation for the region matching by the region and use a ART-I1 competitive learning neural network wh~ch has a learning ability to reflect the topology of the input patterns in order to select characteristic points to describe the shape of a region. Motion vectors for each regions are obtained by matching selected characteristic points. However, the two dimensional image, the projection of the the three dimensional real world, produces fuzziness in motion analysis due to its incompleteness by nature and the error from image segmentation used for extracting information about objects. Therefore, the belief degrees for each regions are calculated using fuzzy reasoning to l-nanipulate uncertainty in motion estimation.