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Application of Fuzzy C-Means Algorithm in Complex Background Image Segmentation of Forensic Science

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 517))

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Abstract

In the field of forensic science, image segmentation is required as a basic and significant stage in forensic image analysis. It is very important to segment the stamp impression image with a complex background precisely. This paper puts forward a feasible and efficient approach for complex background stamp impression image segmentation based on Fuzzy C-Means (FCM) algorithm. The fuzzy feature of forensic image can be handled efficiently using Fuzzy C-Means (FCM) algorithm in the forensic science field. The results of the experiments demonstrate the validity and accuracy of Fuzzy C-Means (FCM) algorithm.

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References

  1. Pal, N.R.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)

    Article  Google Scholar 

  2. Shantaiya, S., Verma, K., Mehta, K.K.: Multiple object clustering using fcm and k-means algorithms. Int. J. Comput. Vis. Robot. 6(4), 331 (2016)

    Article  Google Scholar 

  3. APAKang, J.Y., Gong, C.L., Zhang, W.J.: Fingerprint image segmentation using modified fuzzy c-means algorithm (report). J. Biomed. Sci. Eng. 2(8), 656–660 (2009)

    Google Scholar 

  4. Shi, S.P., Yang, X.U., Qian, H.G., Che, X.U.: Issues related to examination of stamp impressions: identification of stamp impressions forged by high simulation techniques. Chin. J. Forensic Sci. (2008)

    Google Scholar 

  5. Zhen, D., Zhongshan, H., Jingyu, Y., Zhenmin, T., Yongge, W.: A quick fcm algorithm for gray images segmentation. Pattern Recognit. Artif. Intel. (1997)

    Google Scholar 

  6. Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (2002)

    Article  Google Scholar 

  7. Yu, J., Yang, M.: A study on a generalized fcm. Lect. Notes Comput. Sci. 2639, 390–393 (2003)

    Article  Google Scholar 

  8. Boss, R.S.C., Thangavel, K., Daniel, D.A.P.: Mammogram image segmentation using fuzzy clustering. In: International Conference on Pattern Recognition, Informatics and Medical Engineering, vol. 02, pp. 290–295. IEEE (2012)

    Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Plan (2017YFC0822004) and by the Key Project of Basic Scientific Research Service Fee of People’s Public Security University of China (2018JKF220). The authors would like to thank the editorial team and reviewers for supporting this paper.

Heartfelt thanks are also given for the comments and contributions of reviewers and members of the editorial team.

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Authors and Affiliations

  1. Forensic image technology direction, People’s Public Security, University of China (PPSUC), No. 1, South Muxidi Lane, Beijing, Xicheng District, People’s Republic of China

    Zhuang Chen, ChunYu Li, ZhanQing Jiang & Yongqiang Zhao

Authors
  1. Zhuang Chen
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  2. ChunYu Li
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  3. ZhanQing Jiang
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  4. Yongqiang Zhao
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Corresponding author

Correspondence to ChunYu Li .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Chen, Z., Li, C., Jiang, Z., Zhao, Y. (2020). Application of Fuzzy C-Means Algorithm in Complex Background Image Segmentation of Forensic Science. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_27

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  • DOI: https://doi.org/10.1007/978-981-13-6508-9_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6507-2

  • Online ISBN: 978-981-13-6508-9

  • eBook Packages: EngineeringEngineering (R0)

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