A fast algorithm for integrating connected-component labeling and euler number computation
- 180 Downloads
This paper proposes a fast algorithm for integrating connected-component labeling and Euler number computation. Based on graph theory, the Euler number of a binary image in the proposed algorithm is calculated by counting the occurrences of four patterns of the mask for processing foreground pixels in the first scan of a connected-component labeling process, where these four patterns can be found directly without any additional calculation; thus, connected-component labeling and Euler number computation can be integrated more efficiently. Moreover, when computing the Euler number, unlike other conventional algorithms, the proposed algorithm does not need to process background pixels. Experimental results demonstrate that the proposed algorithm is much more efficient than conventional algorithms either for calculating the Euler number alone or simultaneously calculating the Euler number and labeling connected components.
KeywordsGraph theory Euler number Connected-component labeling Pattern recognition Computer vision
We thank the anonymous referees for their valuable comments that greatly improved this paper. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61471227, the Grant-in-Aid for Scientific Research (C) of the Ministry of Education, Science, Sports, and Culture of Japan under Grant No. 26330200, and the Key Science and Technology Program for Social Development of Shaanxi Province, China (Program No. 2014K11-02-01-13).
- 1.Gonzalez, R.C., Woods, R.E.: Digital image processing. Third ed., Pearson Prentice Hall, Upper Saddle River, NJ 07458 (2008)Google Scholar
- 2.Horn, B.P.K.: Robot Vision, pp. 73–77. McGraw-Hill, New York (1986)Google Scholar
- 3.Srihari, S.N.: Document image understanding. In: Proceedings ACM/IEEE Joint Fall Computer Conference, Dallas, TX, pp. 87–95 (1986)Google Scholar
- 4.Rosin, P.L., Ellis, T.: Image difference threshold strategies and shadow detection. In: Proceedings British Machine Vision Conference, pp. 347–356 (1995)Google Scholar
- 10.He, L., Chao, Y., Suzuki, K.: A linear-time two-scan labeling algorithm. Image Processing, 2007. ICIP 2007. IEEE Int. Conf., pp. V-241–V-244, San Antonio (2007)Google Scholar
- 16.Gray, S.B.: Local properties of binary images in two dimensions. IEEE Trans. Comput. C-20:551–561 (1971)Google Scholar
- 20.Zenzo, S., Cinque, L., Levialdi, S.: Run-based algorithms for binary image analysis and processing. IEEE Trans. 18(1), 83–89 (1996)Google Scholar
- 22.He, L., Zhao, X., Yao, B., Yang, Y., Chao, Y., Shi, Z., Suzuki, K.A.: Combinational algorithm for connected-component labeling and euler number computing. J. Real Time Image Process. doi: 10.1007/s11554-014-0433-y
- 23.West, D.B.: Introduction to graph theory. Second edition, Prentice Hall (2001)Google Scholar
- 24.He, L., Yao, B., Zhao, X., Yan, Y., Chao, Y., Ohta A.: A graph-theory-based algorithm for euler number computing. IEICE TRANSACTIONS on Information and Systems. E98-D(2), 457–461 (2015)Google Scholar