• 한국정보과학회논문지:시스템및이론
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• 제26권1호
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• pp.1-8
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• 1999

본 논문에서는 볼륨 렌더링에서 화상영역 수퍼샘플링을 효율적으로 수행시키는 방법을 제시한다. 이 알고리즘은 부화소에서 나오는 평행 광선들 간의 규칙성을 이용하여 각 화소로부터 상대 위치가 같은 부화소에서 나오는 광선마다 한 개씩의 원형을 만들어 둠으로써 수퍼샘플링을 효율적으로 수행한다. 또한 본 알고리즘은 객체순서 알고리즘에 기반을 두고 있으므로 화상순서 알고리즘에서도 수퍼샘플링을 하는 것보다 매우 빠르게 처리하게 된다. 또한 본 논문에서는 적응 수퍼샘플링 방법도 제안한다. 이는 에일리어싱이 생길 가능성이 많은 부분에서만 수퍼샘플링을 하며, 이러한 영역은 분류화 전처리 단게에서 미리 결정해둔다.

• 한국멀티미디어학회논문지
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• 제9권8호
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• pp.971-981
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• 2006

쉬어-왑 분해 렌더링은 볼륨 렌더링 방법 중 가장 빠르지만 영상의 화질이 좋지 않다는 단점이 있다. 본 연구에서는 쉬어-왑 렌더링의 빠른 속도를 유지하며 화질을 개선하는 방법을 제안한다. 화질 개선의 첫 번째 방법은 중간영상(intermediate image) 기반의 수퍼샘플링(supersampling) 기법이다. 객체 좌표와 영상 좌표간의 변환을 효율적으로 수행하여 임의 비율의 영상 확대를 빠르게 수행한다. 화질 개선의 두 번째 방법은 선-적분(pre-integrated) 렌더링을 사용하는 것이다. 기존의 선-적분 기반 쉬어-왑 렌더링은 빈-공간 도약(empty space leaping)을 하지 못하여 속도가 저하되는 문제가 있지만, 본 연구에서 제안하는 겹친 최소-최대 지도(overlapped min-max map) 자료구조를 사용하면 빈-공간 도약을 수행하여 속도 문제를 해결한다. 본 제안 방법을 통해 광선추적법(ray-tracing) 수준의 고화질 렌더링 영상을 빠른 시간에 생성할 수 있다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.422-425
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• 2004

This paper develops an octree-based algorithm for machining simulation. Most commercial machining simulators are based on the Z map model, which has several limitations in terms of achieving a high level of precision in five-axis machining simulation. Octree representation being a three-dimensional (3D) decomposition method, an octree-based algorithm is expected to be able to overcome such limitations. With the octree model, storage requirement is reduced. Moreover, recursive subdivision is processed in the boundaries, which reduces useless computations. The supersampling method is the most common form of antialiasing and is typically used with polygon mesh rendering in computer graphics. The supersampling technique is being used to advance the efficiency of the octree algorithm..

• 한국CDE학회논문집
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• 제10권2호
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• pp.107-113
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• 2005

Octree-based algorithm is developed for machining simulation. Most of commercial machining simulators are based on Z map model, which have several limitations to get a high precision in 5 axis machining simulation. Octree representation is three dimensional decomposition method. So it is expected that these limitations be overcome by using octree based algorithm. By using the octree model, storage requirement is reduced. And also recursive subdivision was processed in the boundaries, which reduces useless computation. The supersampling method is the most common form of the anti-aliasing and usually used with polygon mesh rendering in computer graphics. Supersampling technique is applied for advancing its efficiency of the octree algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.956-959
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• 2005

The overall goal of this thesis is to develop a new algorithm based on the octree model for geometric and mechanistic milling operation at the same time. Most commercial machining simulators are based on the Z map model, which has several limitations in terms of achieving a high level of precision in five-axis machining simulation. Octree representation being a three-dimensional (3D) decomposition method, an octree-based algorithm is expected to be able to overcome such limitations. With the octree model, storage requirement is reduced. Moreover, recursive subdivision is processed in the boundaries, which reduces useless computations. To achieve a high level of accuracy, fast computation time and less memory consumption, the advanced octree model is suggested. By adopting the supersampling technique of computer graphics, the accuracy can be significantly improved at approximately equal computation time. The proposed algorithm can verify the NC machining process and estimate the material removal volume at the same time.

• 한국멀티미디어학회논문지
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• 제17권10호
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• pp.1150-1159
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• 2014

This paper proposes a method for extracting tissue cells from an organism image by an electron microscope and getting the whole cell number and the area from the cell. In general, the difference between the cell color and the background is used to extract tissue cell. However, there may be a problem when overlapped cells are seen as a single cell. To solve the problem, we split them by using cell size and curvature. This method has a 99% accuracy rate. To measure the cell area, we compute two areas, the inside and boundary of the cell. The inside is simply calculated by the number of pixels. The cell boundary is obtained by applying super sampling, linear interpolation, and cubic spline interpolation. It improves the error rate, 18%, 19%, and 120% respectively, in comparison to the counting method that counts a pixel area as 1.