• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.151-154
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• 2001

본 논문의 목적은 범용 USB 카메라 입력 영상으로부터 실시간으로 얼굴을 추적하는 시스템을 구현하는데 있다. 먼저 USB 카메라로부터 영상을 입력받은 후 2차원 RGB 컬러 모델링으로 추출한 살색 영역을 찾고 가로, 세로 프로젝선 정보를 이용하여 얼굴을 찾는다. 기존의 RGB 컬러 모델을 개선하여 빛에 강인한 모델링을 하였으며, 프로젝션 정보를 이용할 때 일어나는 에러를 최소화하기 위하여 누적 히스토그램 영역 결합 알고리즘을 제안하였다. 구현한 시스템은 움직임이 많은 영상에도 빠른 속도를 보였으며, 특히 영상의 움직임이적은 경우 카메라에서 영상을 보여 주는 것과 동시에 얼굴을 찾아내어, 연속적인 프레임을 처리할 수 있는 성능을 보였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.234-237
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• 2009

많은 건설 현장에서 카메라와 CCTV(Closed-circuit Television)와 같은 장비를 활용하여 건설 현장의 상황을 모니터링 하고 있다. 하지만 많은 작업이 실외에서 이루어지는 토목 건축공사의 특성상 적절한 수준의 영상 데이터를 축적하는 것은 쉽지 않은 일이다. 특히, 이미지 프로세싱기법을 사용 하여 자동화된 건설 관리의 수행 시, 영상 데이터의 품질에 따라 에러가 발생하여 건설 관리자가 잘못된 정보를 얻게 될 경우도 발생하게 된다. 본 연구에서는 케니엣지(Canny Edge) 인식기법과 워터쉐드(Watershed) 변환, 그리고 3D CAD Mask를 이용한 건축 구조물 기둥의 시공 상황 분석 기법에 근거하여, 영상 데이터 분석 시 오류를 최소화하기 위한 에러 제거 알고리즘을 제시한다. 실제 데이터와 비교를 통하여 그 활용 가능성 또한 검증한다.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1543-1550
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• 2004

Modeling from images is a cost-effective means of obtaining 3D geometric models. These models can be effectively constructed from classical Structure from Motion algorithm. However, it's too difficult to reconstruct whole scenes using SFM method since general sites contain a very complex shapes and brilliant colours. To overcome this difficulty, the current paper proposes a new reconstruction method based on a moving Planar mirror. We devise the mirror posture instead of scene itself as a cue for reconstructing the geometry That implies that the geometric cues are inserted into the scene by compulsion. With this method, we can obtain the geometric details regardless of the scene complexity. For this purpose, we first capture image sequences through the moving mirror containing the interested scene, and then calibrate the camera through the mirror's posture. Since the calibration results are still inaccurate due to the detection error, the camera pose is revised using frame-correspondence of the comer points that are easily obtained using the initial camera posture. Finally, 3D information is computed from a set of calibrated image sequences. We validate our approach with a set of experiments on some complex objects.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.294-298
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• 2009

In this study, we proposes the method to improve the quality of the image through the edge extraction more delicately. Our method is named ESII(Edge Sensitive Image Interpolation) and doesn't use the fixed parameter of the interpolation kernel. However, it changes the parameter of pixel which is interpolated to the high definition image using the proper information from the surrounding pixels. It reconstructs the image by using the LSE(Least Square Error) and determining the pixel values to make the CME(Camera Modelling Error) minimized. Compared to the conventional methods, suggested method shows the higher quality of subjective and objective image definition and lessons the computational complexity by separating the image into 1-D data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.1-6
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• 2010

The lens distortion in the machine vision system is inevitable phenomenon. Distortion is getting worse, due to the selection of lens in the trend of reducing prices and size of the system. In this trend, the distortion correction becomes more important. But, the traditional correction methods has problems, such as complexity and requiring more operations. Effective distorted digital image correction is the precondition of target detection and recognition based on vision inspection. To overcome the disadvantage of traditional distortion correction algorithms, such as complex modeling, massive computation and marginal information loss, an image distortion correction algorithm based on photogrammetry method is proposed in this paper. In our method, we use the lattice image as the measurement target. Through the experimental results, we could find that we can reduce the processing time by 4ms. And also the inspection failure rate of our method was reduced by 2.3% than human-eyes inspection method.