Abstract
A multi-object segmentation algorithm based on Background Modeling and Region Growing (named as BMRG) algorithm is proposed in this paper. For multi-object segmentation, the algorithm uses Chebyshev inequality and the kernel density estimation method to do background modeling firstly. Then in order to classify image pixels as background points, foreground points and suspicious points, an adaptive threshold algorithm is proposed accordingly. After using background subtraction to get the ideal foreground image, region growing method is used for multi-object segmentation. Here, we improved the region growing method by introducing the growth seed concept for multi-object segmentation, which is calculated from the sparse matrix of quad-tree decomposition. Experimental results show that Chebyshev inequalities can quickly distinguish the foreground and background points. Multi-object segmentation results are satisfactory through seed-based region growing method. Comparison and analysis the experimental results show that the proposed BMRG algorithm is feasible, rapid and effective.
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References
Kaiyan, L., Junhui, W., Lihong, X.: Discussion of color image segmentation. Journal of Image and Graphics 10 (2005)
Yin, G., Dayou, L., Hong, Q., He, L.: A cluster way of K mean. Journal of Software 19(11), 2814–2821 (2008)
Ubukata, T., Terabayashi, K., Moro, A., Umeda, K.: Multi-Object Segmentation in a Projection Plane Using Subtraction Stereo. In: 2010 International Conference on Pattern Recognition, pp. 3296–3299 (2010)
Verma1, O.P., Hanmandlu, M., Susan, S., et al.: A Simple Single Seeded Region Growing Algorithm for Color Image Segmentation using Adaptive Thresholding. In: 2011 International Conference on Communication Systems and Network Technologies, pp. 500–503 (2011)
Xiaofang, L., Dansong, C., Xianglong, T., Jiafeng, L.: Multi-object Segmentation Based on Pulse Coupled Neural Network. In: 2008 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 744–748 (2008)
Badenas, J., Bober, M., Pla, F.: Segmenting traffic scenes from grey level and motion information. Pattern Analysis and Applications 4, 28–38 (2001)
Csaba, B., David, S.: Multiple object tracking by hierarchical association of spatio—temporal data. In: 17th IEEE International Conference on Image Processing, pp. 41–44 (2010)
Shuifa, Z., Wensheng, Z., Huan, D., Liu, Y.: Background modeling and object detecting based on optical flow velocity field. Journal of Image and Graphics 16, 236–243 (2011)
Jean, G., Akio, K., Avinash, C.K.: A multi-Kalman filtering approach for video Tracking of human-delineated objects in cluttered environments. Computer Vision and Image Understanding 102, 260–316 (2006)
Elgammal, A., Duraiswami, R., Harwood, D., et al.: Back ground and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90, 1151–1163 (2002)
Jiandong, G., Zhi, L., Zhaoyang, Z.: A Novel Moving Object Segmentation Algorithm Using Kernel Density Estimation and Edge Information. Journal of Computer-aided Design & Computer Graphics 21, 223–228 (2009)
Qin, A.K., David, A.: Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty. IEEE Transactions on Image Processing 19, 2157–2170 (2010)
Kun, Z., Cuirong, W.: A Novel Moving Object Segmentation Algorithm Based on Chebyshev Inequality. In: Proceedings of 2011 International Conference on Opto-Electronics Engineering and Information Science, pp. 1346–1350 (2011)
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Zhang, K., Wang, C., Wang, B. (2012). A Multi-object Segmentation Algorithm Based on Background Modeling and Region Growing. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_13
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DOI: https://doi.org/10.1007/978-3-642-31346-2_13
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