• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.8
• /
• pp.1805-1810
• /
• 2015

Increasing of depth data accessibility, depth data is used in many researches. Motion recognition of computer vision also widely use depth image. More accuracy motion recognition system needs more stable depth data. But depth sensor has a noise. This noise affect accuracy of the motion recognition system, we should noise suppression. In this paper, we propose using spatial domain and temporal domain stabilization for depth image and makes it hardware IP. We adapted our hardware to floor removing algorithm and verification its effect. we did realtime verification using FPGA and APU. Designed hardware has maximum frequency 202.184MHz.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.76-82
• /
• 2012

As the portable camcorder becomes popular, various video stabilization algorithms for de-shaking of camera motion have been developed. In the past, most video stabilization algorithms were based on 2-dimensional camera motion, but recent algorithms show much better performance by considering 3-dimensional camera motion. Among the previous video stabilization algorithms, 3D video stabilization algorithm using content-preserving warps is known as the state-of-the art owing to its superior performance. But, the major demerit of the algorithm is its high computational complexity. So, we present a computationally light full-frame warping algorithm based on ROI (region-of-interest) while providing comparable visual quality to the state-of-the art in terms of ROI. First, a proper ROI with a target depth is chosen for each frame, and full-frame warping based on the selected ROI is applied.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.8
• /
• pp.132-139
• /
• 2015

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.