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박현우,백두원,정문렬,Park, Hyun-Woo,Paik, Doo-Won,Jung, Moon-Ryul 한국정보처리학회 2000 정보처리논문지 Vol.7 No.2
본 논문에서는 체적 시각화 과정을 이론적으로 고찰하여 시각화 모델을 제시하고 그 모델로 유도된 관계로부터 미분 방정식을 이용하여 분석적 체적 시각화 해법을 구하였다. 이 분석적 방법을 제적 시각화의 대표적인 방법인 Levoy의 이산적 광선 추적법과 비교하여 본 연구에서 제시한 방법의 특수한 형태가 Levoy의 이산적 방법임을 보였다. 그리고 체적 데이터를 시각화기 위해서는 사용자가 시각화하기를 원하는 부위를 선택하고 이 부분만을 추출하는 영역 분할 작업이 필요하다. 본 논문에서는 영역확장법에 기반을 둔 효율적인 3차원 영역 분할 기법을 개발하여 위의 분석적인 방법을 이용하여 3차원 제적 데이터의 시각화를 위한 시스템을 구현하였다. 그리고 본 접근법에 대한 의의와 유용함에 대한 가설적인 결론을 구현된 시스템을 이용한 실험에 근거하여 유도하였다. Lovoy의 이산적인 방법과 분석적인 방법을 같은 데이터에 대해 3차원 영역 분할 수행 후 적용한 실험은 분석적인 방법이 이산적인 방법에 비해 렌더링된 이미지의 질이 더 좋음을 보여준다. When volume data is visualized by the ray casting method, the color value of each pixel in the image is obtained by composing the color contributions of the sample points that lie on the ray cast from the pixel point. In most ray tracing methods including Levoy's classical method, the color composition is formulated as a summation of the color contributions of the discrete sample points. However, the more precise color composition is formulated as differential equations over the color contributions of the continuous sample points. The discrete formulation is used, because analytical solutions to the continuous formulations are hard to find. In this paper, however, we have discovered a semi-analytical solution to the continuous formulation of a typical ray tracing of volume data. We have applied both Levoy's method and ours to the same set of data, and compared the visual quality of both results. The comparison shows that our method produces a more fine-grained visualization of volume data.
스테레오 비전 기반 Light Drawing 시스템 구현
박원배(Won-Bae Park),박창범(Chang-Bum Park),백두원(Doo-Won Paik) 한국콘텐츠학회 2010 한국콘텐츠학회논문지 Vol.10 No.2
Light Drawing은 사진 촬영 기법 중 하나로, 어두운 방이나 밤에 빛이 나는 물체의 움직임을 노출 기법을 이용하여 촬영 한 것이다[1]. 사용자가 Light Drawing을 만들려 한다면 장 노출 카메라가 필요하고, 어두운 환경이 필요함으로 제한사항이 따르며 사용자는 3차원 공간에 그림을 그리는 것에 어려움을 느낀다. 반면에 포토샾과 같은 컴퓨터 드로잉 툴을 사용하여 Light Drawing 효과를 낼 수 있다. 그러나 마우스나, 타블렛과 같은 입력 장치는 실제로 그림을 그리는 행위와 차이가 나기에 사용자들의 흥미를 반감시킨다. 본 논문에서는 Light Drawing을 손쉽게 제작 가능한 디지털 컨텐츠를 제안한다. 스테레오 비전을 이용하여 빛의 3차원 위치 정보를 계산하고 Drawing Effect를 이용하여 3차원 가상 공간에 Light Drawing을 구현하였다. Light Drawing is a photographic technique which exposures are made at night or in a darkened room usually by moving a hand-held light source[1]. Due to the limitations of equipment and environment, users having difficulty in drawing a picture in 3D space. If user take a light drawing, they need a camera that have function and darkened environment. Alternative solution is that we can make a light drawing picture by using the computer drawing tool as in Photoshop. Nevertheless, this solution will let the User lose their interest in drawing because this solution cannot synchronize between the real action of human hand motion and the electronic input devices such as mouse and keyboard. This paper proposed a digital content that can make light drawing easier. We used a digital content that will facility Light Drawing easier. We can measure the light spot position by using the stereo camera. Based on the measured position of the light spot, we reproduce light drawing in virtual space by using drawing effect method.
윤진성,김관중,김계영,백두원,Yoon, Jin-Sung,Kim, Kwan-Jung,Kim, Gye-Young,Paik, Doo-Won 한국정보처리학회 2003 정보처리학회논문지B Vol.10 No.5
An active contour model called snake is powerful tool for object contour extraction. But, conventional snakes require exhaustive computing time, sometimes can´t extract complex shape contours due to the properties of energy function, and are also heavily dependent on the position and the shape of an initial snake. To solving these problems, we propose in this paper an improved snake called "shaking snake", based on a greedy algorithm. A shaking snake consist of two steps. According to their appropriateness, we in the first step move each points directly to locations where contours are likely to be located. In the second step, we then align some snake points with a tolerable bound in order to prevent local minima. These processes shake the proposed snake. In the experimental results, we show the process of shaking the proposed shake and comparable performance with a greedy snake. The proposed snake can extract complex shape contours very accurately and run fast, approximately by the factor of five times, than a greedy snake.
정성기(Sung-Gi Jung),백두원(Doo-Won Paik),최형일(Hyung-Il Choi) 한국컴퓨터정보학회 2015 韓國컴퓨터情報學會論文誌 Vol.20 No.12
In this paper, we propose a method of glasses detection in facial image. we develop a detection method of glasses with a weighted sum of the results that detected by facial element detection and glasses frame candidate region. Component of the face detection method detects the glasses, by defining the detection probability of the glasses according to the detection of a face component. Method using the candidate region of the glasses frame detects the glasses, by defining feature of the glasses frame in the candidate region. finally, The results of the combined weight of both methods are obtained. The proposed method in this paper is expected to increase security system’s recognition on facial accessories by raising detection performance of glasses or sunglasses for using ATM
박현우(Hyun Woo Park),백두원(Doo Won Paik),정문렬(Moon Ryul Jung) 한국정보처리학회 2000 정보처리학회논문지 Vol.7 No.2
When volume data is visualized by the ray casting method, the color value of each pixel in the image is obtained by composing the color contributions of the sample points that lie on the ray cast from the pixel point. In most ray tracing methods including Levoy's classical method, the color composition is formulated as a summation of the color contributions of the discrete sample points. However, the more precise color composition is formulated as differential equations over the color contributions of the continuous sample points. The discrete formulation is used, because analytical solutions to the continuous formulations are hard to find. In this paper, however, we have discovered a semi-analytical solution to the continuous formulation of a typical ray tracing of volume data. We have applied both Levoy's method and ours to the same set of data, and compared the visual quality of both results. The comparison shows that our method produces a more fine-grained visualization of volume data.