@inproceedings { JMMY,
     author        = { Lv Jianxun and Mahmoud Mohamed and B{"a}rbel Mertsching and Haiwen Yuan },
     title         = { Dense Optical Flow Estimation from RGB-D },
     month         = { August },
     year          = { 2017 },
     booktitle     = { 7th International Conference on Instrumentation {\&} Measurement, Computer, Communication and Control (IMCCC), Changchun, China },
     abstract      = { Optical flow is a key problem in computer vision with tremendous potential applications in many fields, such as action recognition, autonomous navigation and manipulation. In this paper, we propose a dense optical flow estimation approach for objects of interest. In order to improve the accuracy of the optical flow estimation, the intensity and depth data from the RGB-D sensor are used for doing object segmentation. Afterwards, a homography based method assuming the surface to be planar is applied to obtain dense optical flow for each segment. Several experiments have been performed to evaluate the proposed method. The results demonstrate the validity of our approach. }
}