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Estimating Illumination Distribution to Generate Realistic Shadows in Augmented Reality
( Changkyoung Eem ),( Iksu Kim ),( Yeongseok Jung ),( Hyunki Hong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.6
Mobile devices are becoming powerful enough to realize augmented reality (AR) application. This paper introduces two AR methods to estimate an environmental illumination distribution of a scene. In the first method, we extract the lighting direction and intensity from input images captured with a front-side camera of a mobile device, using its orientation sensor. The second method extracts shadow regions cast by three dimensional (3D) AR marker of known shape and size. Because previous methods examine per pixel shadow intensity, their performances are much affected by the number of sampling points, positions, and threshold values. By using a simple binary operation between the previously clustered shadow regions and the threshold real shadow regions, we can compute efficiently their relative area proportions according to threshold values. This area-based method can overcome point sampling problem and threshold value selection. Experiment results demonstrate that the proposed methods generate natural image with multiple smooth shadows in real-time.
증강현실에서 사실적인 그림자 생성을 위한 조명 분포 모델의 계층적 분할
김익수(Iksu Kim),임창경(Changkyoung Eem),홍현기(Hyunki Hong) 한국방송·미디어공학회 2016 방송공학회논문지 Vol.21 No.1
By estimating environment light distribution, we can generate realistic shadow images in AR(augmented reality). When we estimate light distribution without sensing equipment, environment light model, geometry of virtual object, and surface reflection property are needed. Previous study using 3D marker builds surrounding light environment with a geodesic dome model and analyzes shadow images. Because this method employs candidate shadow maps in initial scene setup, however, it is difficult to estimate precise light information. This paper presents a novel light estimation method based on hierarchical light distribution model subdivision. By using an overlapping area ratio of the segmented shadow and candidate shadow map, we can make hierarchical subdivision of light geodesic dome.
Using Real-time Stereo Matching for Human Gesture Detection and Tracking
Sungil Kang,Annah Roh,Changkyoung Eem,Hyunki Hong 중앙대학교 영상콘텐츠융합연구소 2014 TechArt :Journal of Arts and Imaging Science Vol.1 No.1
This paper presents a human gesture detection and tracking system using real-time stereo matching. A disparity map is obtained from stereo matching based on general-purpose computing of graphics processing units in real-time, and then, 3-D foreground blobs are generated using depth information gathered from the map. The distribution of the 3-D foreground blobs, combined with detection of the face and torso, is applied to determine the position of the human body. A skeleton model for the upper body is successively fitted to a median axis in the area that has more 3-D blobs from the shoulder area to the hands. The position and color information obtained from the 3-D blobs for a robust tracking of arm and hand gestures are examined. The reconstructed trajectory is then classified into one of eight gesture reference sets.
시차변화(Disparity Change)와 장면의 부분 분할을 이용한 SLAM 방법
최재우(Jaewoo Choi),이철희(Chulhee Lee),임창경(Changkyoung Eem),홍현기(Hyunki Hong) 대한전자공학회 2015 전자공학회논문지 Vol.52 No.8
카메라를 이용하는 시각(visual) SLAM(Simultaneous Localization And Mapping)은 로봇의 위치 등을 파악하는데 널리 이용되고 있다. 일반적으로 시각 SLAM은 움직임이 없는 고정된 특징점을 대상으로 연속적인 시퀀스 상에서 카메라의 움직임을 추정한다. 따라서 이동하는 객체가 많이 존재하는 상황에서는 안정적인 결과를 기대하기 어렵다. 본 논문에서는 이동 객체가 많은 상황에서 스테레오 카메라를 이용한 SLAM을 안정화하는 방법을 제안한다. 먼저, 스테레오 카메라를 이용하여 깊이영상을 추출하고 옵티컬 플로우를 계산한다. 그리고 좌우 영상의 옵티컬 플로우를 이용하여 시차변화(disparity change)를 계산한다. 그리고 깊이 영상에서 사람과 같이 움직이는 객체에 대한 ROI(Region Of Interest)를 구한다. 실내 상황에서는 벽과 같은 정적인 평면들이 움직이는 영역으로 잘못 판단되는 경우가 자주 발생한다. 이런 문제점을 해결하기 위해 깊이 영상을 X-Z 평면으로 사영하고 허프(hough) 변환하여 장면을 구성하는 평면을 결정한다. 앞의 과정에서 판단된 이동 객체 중에서 벽과 같은 장면 요소를 제외한다. 제안된 방법을 통해 정적인 특징점이 요구되는 SLAM의 성능을 보다 안정화할 수 있음을 확인하였다. Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot’s location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.