Skip to main content
SpringerLink
Log in

An adaptive multi-feature segmentation model for infrared image

  • Regular Paper
  • Published:
Optical Review Aims and scope Submit manuscript

Abstract

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1, 321–331 (1988)

    Article  MATH  Google Scholar 

  2. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22, 61–79 (1997)

    Article  MATH  Google Scholar 

  3. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)

    Article  ADS  MATH  Google Scholar 

  4. Wang, X.-F., Huang, D.-S., Xu, H.: An efficient local Chan–Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010). doi:10.1016/j.patcog.2009.08.002

    Article  MATH  Google Scholar 

  5. Li, M., He, C., Zhan, Y.: Tensor diffusion level set method for infrared targets contours extraction. Infrared Phys. Technol. 55, 19–25 (2012). doi:10.1016/j.infrared.2011.08.009

    Article  ADS  Google Scholar 

  6. Zhang, K., Zhang, L., Song, H.: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis. Comput. 28, 668–676 (2010)

    Article  Google Scholar 

  7. Li, C., Kao, C.Y., Gore, J.C.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17, 1940–1949 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  8. Shawn, L., Allen, T.: Localizing region-based active contours. IEEE Trans. Image Process. 17, 2029–2039 (2008)

    Article  MathSciNet  Google Scholar 

  9. Liu, S., Peng, Y.: A local region-based Chan–Vese model for image segmentation. Pattern Recogn. 45, 2769–2779 (2012)

    Article  MATH  Google Scholar 

  10. Tian, Y., Zhou, M.-Q., Wu, Z.-K., Wang, X.-C.: A region-based active contour model for image segmentation. In: International Conference on Computational Intelligence and Security, 2009 (CIS’09), pp. 376–380 (2009)

  11. Ping, W., Kaiqiong, S., Zhen, C.: Local and global intensity information integrated geodesic model for image segmentation. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 129–132 (2012)

  12. Li, D., Li, W., Liao, Q.: Active contours driven by local probability distributions, J. Vis. Commun. Image Represent. 24, 522–533 (2013)

    Article  Google Scholar 

  13. Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput. Med. Imaging Graph. 33(7), 520–531 (2009). doi:10.1016/j.compmedimag.2009.04.010

    Article  Google Scholar 

  14. Li, C.M., Kao, C.Y., Gore, J.C., Ding, Z.H.: Implicit active contours driven by local binary fitting energy. In: Proceedings of the CVPR, pp. 339–345. IEEE (2007)

  15. Chunming, L., Chenyang, X., Changfeng, G., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), vol. 431, pp. 430–436 (2005)

  16. Zhang, K., Song, H., Zhang, L.: Active contours driven by local image fitting energy. Pattern Recogn. 43, 1199–1206 (2010). doi:10.1016/j.patcog.2009.10.010

    Article  MATH  Google Scholar 

  17. He, L., Zheng, S., Wang, L.: Integrating local distribution information with level set for boundary extraction. J. Vis. Commun. Image Represent. 21, 343–354 (2010). doi:10.1016/j.jvcir.2010.02.009

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundations of China (61231014 and 61373061).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, 210094, China

    Tingting Zhang, Jin Han, Yi Zhang & Lianfa Bai

Authors
  1. Tingting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianfa Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianfa Bai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Han, J., Zhang, Y. et al. An adaptive multi-feature segmentation model for infrared image. Opt Rev 23, 220–230 (2016). https://doi.org/10.1007/s10043-016-0190-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10043-016-0190-1

Keywords

Search

Navigation