Image Object Segmentation Algorithm Combining Color and Depth Information
ZHENG Qingqing1, WU Jin1, WEI Longsheng2, LIU Jin1,3
1.School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081 2.School of Automation, China University of Geosciences, Wuhan 430074 3.School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191
Abstract：The object contour is difficult to be extracted by the existing methods only using appearance information in the image with shadow, low-contrast edges and ambiguous areas. The depth discontinuities provide useful information for object boundaries identification. An image object segmentation algorithm is proposed by combining color and depth information. Firstly, the image is over-segmented into small homogeneous regions by mean-shift algorithm, and then color and depth information are combined to describe the characteristics of regions adequately. Next, seed regions of target and background are automatically selected according to depth information. Finally, an object contour is extracted by maximal similarity based region merging (MSRM). Experiment results on Middlebury and NYU-V2 databases show that the proposed algorithm is simple and effective compared with state-of-the-art algorithms. Besides, it improves the segmentation accuracy and enhances the visual effect of the segmentation image.
2016, Vol. 29 
