On-line object segmentation through human-robot interaction
Soohwan Kim, Dong Hwan Kim, Sung-Kee Park · 2010
In this paper we propose a new method for on-line object segmentation through human-robot interaction. Particularly, we define three types of human gestures for object learning by the size of target objects; holding small objects, pointing at medium ones and contacting two corners of large ones. The regions of interest where objects are likely to be located are interpreted from those gestures and represented as rectangles in captured images. For object segmentation, we suggest a marker-based watershed segmentation method which segregates an object within a region of interest in real-time performance. Experimental results show that the segmentation quality of our method is as good as that of the GrabCut algorithm, but the computational time of ours is so much faster that it is appropriate for practical applications.