Abstract The image segmentation using stereo image pairs is discussed. An image segmentation algorithm combining depth discontinuities and color information is proposed. Mean-shift segmentation algorithm is applied to the over-segmentation of the image,and meanwhile the dense depth map of the image pairs can be calculated by using stereo vision algorithm. Then,through combining color image over-segmentation and depth discontinuities,multiple seed regions for accurate segmentation are selected along the depth discontinuities. By using graph cut algorithm,unlabeled regions are assigned with seed regions′ labels. Next,the neighbor regions with different labels but without discontinuous depth boundary between them are merged together as well. Compared with the traditional feature clustering image segmentation algorithms,the proposed algorithm overcomes the problems of over-segmentation and under-segmentation,and semantic object segmentation results can be achieved. Experimental results show the validity of the proposed algorithm.
[1] Fischler M A,Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM,1981,24(6): 381-395 [2] Carson C,Belongie S,Greenspan H,et al. Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(8): 1026-1038 [3] Comaniciu D,Meer P. Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(5): 603-619 [4] Felzenszwalb P,Huttenlocher D. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision,2004,59(2): 167-181 [5] Boykov Y Y,Jolly M P. Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images // Proc of the 8th International Conference on Computer Vision. Vancouver,Canada,2001: 105-112 [6] Shi Jianbo,Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence,2000,22(8): 888-905 [7] Gordan G,Darrel T,Harville M,et al. Background Estimation and Removal Based on Range and Color // Proc of the Conference on Computer Vision and Pattern Recognition. Fort Collins,USA,1999,II: 464-472 [8] Cigla C,Alatan A A. Depth Assisted Object Segmentation in Multi-View Video // Proc of the 3DTV Conference on the True Vision-Capture,Transmission and Display of 3D Video. Istanbul,Turkey,2008: 185-188 [9] Cigla C,Alatan A A. Segmentation in Multi-View Video via Color,Depth and Motion Cues // Proc of the 15th IEEE International Conference on Image Processing. San Diego,USA,2008: 2724-2727 [10] Doulamis N D,Doulamis A D,Avrithis Y S,et al. Efficient Summarization of Stereoscopic Video Sequences. IEEE Trans on Circuits and Systems for Video Technology,2000,10(4): 501-517 [11] Doulamis A D,Doulamis N D,Ntalianis K S,et al. Unsupervised Semantic Object Segmentation of Stereoscopic Video Sequences // Proc of the International Conference on Information Intelligence and Systems. Bethesda,USA,1999: 527-533 [12] Zhang Qian,Liu Suxing,An Ping,et al. Object Segmentation Based on Disparity Estimation // Proc of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation. Shanghai,China,2009: 1053-1056 [13] An Ping,Lu Chaohui,Zhang Zhaoyang. Object Segmentation Using Stereo Images // Proc of the International Conference on Communications,Circuits and Systems. Chengdu, China,2004,I: 534-538 [14] Scharstein D,Szeliski R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision,2002,47(1/2/3): 7-42 [15] Wang Zengfu,Zheng Zhigang. A Region Based Stereo Matching Algorithm Using Cooperative Optimization // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Alaska,USA,2008: 1-8 [16] Huang Xiaofei. Cooperative Optimization for Energy Minimization: A Case Study of Stereo Matching [EB/OL]. [2007-01-09]. http://arxiv.org/pdf/cs.CV/0701057 [17] Martin D R,Fowlkes C C,Malik J. Learning to Detect Natural Image Boundaries Using Local Brightness,Color and Texture Cues. IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(5): 530-549 [18] Boykov Y,Veksler O,Zabih R. Fast Approximate Energy Minimization via Graph Cuts. IEEE Trans on Pattern Analysis and Machine Intelligence,2001,23(11): 1222-1239 [19] Grundmann M,Kwatra V,Han M,et al. Efficient Hierarchical Graph-Based Video Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco,USA,2010: 2141-2148 [20] Rubner Y,Tomasi C,Guibas L J. A Metric for Distributions with Applications to Image Databases // Proc of the 6th International Conference on Computer Vision. Bombay,India,1998: 59-66 [21] Rother C,Kolmogorov V,Blake A. GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts. ACM Trans on Graphics,2004,23(3): 309-314
2013, Vol. 26 
