• Journal of Digital Convergence
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• v.14 no.12
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• pp.231-236
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• 2016

Currently, a traffic monitoring service using a surveillance camera is provided through internet. In general, if the user points a certain location on a map, then this service shows the real-time image of the camera where it is mounted. In this paper, we proposed the intuitive surveillance monitoring system which displays a real-time camera image on the map by warping with bird's-eye view and with the top of image as the north. In order to robustly estimate the road plane using camera image, we used the motion vectors which can be detected to changes in brightness. We applied a re-adjustment process to have the same directivity with a map and presented a user-friendly interface that can be displayed on the map. In the experiment, the proposed method was presented as the result of warping image that the user can easily perceive like a map.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.907-909
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• 2005

체감형 게임의 재미를 극대화 하기 위해서 몰입감있는 게임화면이 제공되어야 한다. 곡면, 반구형 스크린은 일반 평면 스크린을 사용할 때 보다 더 높은 몰입감을 줄 수 있지만 투사시 생기는 왜곡영상 보정에 많은 계산이 요구되는 문제가 있다. 정지된 영상(프리젠테이션 화면)이나 비디오 영상은 메모리 버퍼링을 통해 시스템이 요구하는 속도를 보장할 수 있으나, 사용자와 상호작용이 요구되는 게임에서는 사용자의 반응을 처리한 후 매 프레임 마다 빠른 워핑(warping) 처리를 요구하게 되는데, 렌더링될 화면을 미리 보정할 수가 없기 때문에 시스템 처리속도 저하의 원인이 된다. 본 논문에서는 체감형 게임을 곡면에 투사하는 방법과 GPU를 이용한 빠른 영상 보정을 제안함으로써 게임에서 요구하는 처리 속도를 보장하고 고해상도의 게임화면을 사용할 수 있도록 했다. 웹카메라를 이용하여 스크린과 프로젝터될 영상간의 호모그래피(homographpy)를 정의해서 워핑했고 워핑 연산을 HLSL로 작성하여 GPU를 이용했다. 실험결과로 반구형 스크린에서 몰입감 있는 AR 탁구게임을 보인다.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.36-42
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• 2016

Recently, many 3D contents have been produced through the multiview camera system. In this system, since a difference of the viewpoint between color and depth cameras is inevitable, the camera parameter plays the important role to adjust the viewpoint as a preprocessing step. The conventional camera calibration method is inconvenient to users since we need to choose pattern features manually after capturing a planar chessboard with various poses. Therefore, we propose a semi-auto camera calibration method using a circular sampling and an homography estimation. Firstly, The proposed method extracts the candidates of the pattern features from the images by FAST corner detector. Next, we reduce the amount of the candidates by the circular sampling and obtain the complete point cloud by the homography estimation. Lastly, we compute the accurate position having the sub-pixel accuracy of the pattern features by the approximation of the hyper parabola surface. We investigated which factor affects the result of the pattern feature detection at each step. Compared to the conventional method, we found the proposed method released the inconvenience of the manual operation but maintained the accuracy of the camera parameters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5854-5860
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• 2014

The on-site performance of a vision-based displacement measurement system (VDMS) was evaluated through a field test on a bridge. The VDMS used in this study is composed of a camera, a marker, a frame grabber, and a laptop. The system measures the displacement by attaching a marker at the location to be measured on the structure, by capturing images of that marker with a fixed rate, and by processing a series of images using a planar homography technique. The developed system was first validated from a laboratory test using a small-scale building structure. The VDMS was then employed in a field test on a railroad bridge with a KTX train running under various conditions. The on-site performance was evaluated by comparing the obtained displacement using the VDMS with the displacement measured from a laser Doppler vibrometer (LDV), which is an expensive and accurate displacement measurement device.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.25-33
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• 2010

In this paper, we propose a simple and efficient algorithm for head pose estimation using a single camera. First, four subimages are obtained from the camera image for face feature extraction. These subimages are used as feature templates. The templates are then tracked by Kalman filtering, and camera projective matrix is computed by the projective mapping between the templates and their coordinate in the 3D coordinate system. And the user's face pose is estimated from the projective mapping between the user's face and image plane. The accuracy and the robustness of our technique is verified on the experimental results of several real video sequences.