Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5181–5191 | Cite as

Pavement image segmentation based on fast FCM clustering with spatial information in internet of things

  • Guofeng QinEmail author
  • Qiutao Li
Article
  • 122 Downloads

Abstract

Pavement image segmentation needs to deal with noise spots and has real time requirement. The original FCM method only considers the pixel’s gray value and doesn’t fully utilize the spatial information of the image. A new fast FCM algorithm is proposed, and it has noise immunity. By comparing with other FCM algorithms, it achieves better segmentation results through less iteration times and more rapid runtime. It is an effective and noise-resistant algorithm for pavement image segmentation from video multimedia in IOT (internet of things) platform.

Keywords

Pavement image segmentation Fast FCM Spatial information Noise immunity IOT 

Notes

Acknowledgements

Foundation item: The National 863 program in Ministry of Science and Technology of the People’s Republic of China (No.: 2013AA040302). Authors are grateful to the Ministry of Science and Technology of the People’s Republic of China for financial support to carry out this work.

References

  1. 1.
    Ahmed MN, Yamany SM, Mohamed N et al (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Trans Med Imaging 21(3):193–199CrossRefGoogle Scholar
  2. 2.
    Benaichouche AN, Oulhadj H, Siarry P (2013) Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction[J]. Digital Signal Process 23(5):1390–1400MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recogn 40(3):825–838CrossRefGoogle Scholar
  4. 4.
    Chen S, Zhang D (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Trans Syst Man Cybern B Cybern 34(4):1907–1916CrossRefGoogle Scholar
  5. 5.
    Chuang KS, Tzeng HL, Chen S et al (2006) Fuzzy c-means clustering with spatial information for image segmentation[J]. Comput Med Imaging Graph 30(1):9–15CrossRefGoogle Scholar
  6. 6.
    Izakian H, Pedrycz W, Jamal I (2013) Clustering spatiotemporal data: an augmented fuzzy c-means[J]. IEEE Trans Fuzzy Syst 21(5):855–868CrossRefGoogle Scholar
  7. 7.
    Li Y, Yu F (2009) A new validity function for fuzzy clustering[C]. Computational Intelligence and Natural Computing, 2009. CINC’09, vol 1. International conference on. IEEE, pp 462–465Google Scholar
  8. 8.
    Ming L (2010) Image segmentation algorithm research and improvement[C]//2010 3rd international conference on advanced Computer theory and Engineering (ICACTE), p 5Google Scholar
  9. 9.
    Tolias Y, Panas SM (1998) Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions[J]. IEEE Trans Syst Man Cybern Syst Hum 28(3):359–369CrossRefGoogle Scholar
  10. 10.
    Wang X, Qin G (2012) Pavement image segmentation based on FCM algorithm using neighborhood information[J]. TELKOMNIKA Indonesian Journal of Electrical Engineering 10(7):1610–1614Google Scholar
  11. 11.
    Yong Y, Chongxun Z, Pan L (2004) A novel fuzzy c-means clustering algorithm for image thresholding[J]. Measurement Science Review 4(1):11–19.SGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina

Personalised recommendations