Skip to main content
Log in

Multi-scale contour detection model based on fixational eye movement mechanism

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Physiological evidence has shown that classical receptive field (CRF) responses in the primary visual cortex (V1) can be suppressed by its surrounding region, called the non-classical receptive field (nCRF). Currently, the contour detection model based on the physiological characteristics of the V1 region is mainly used to suppress texture and highlight contour information through the inhibition of nCRF features. However, the effect of eye movement on inhibition is not considered in the inhibition calculation of such models. Inspired by the fixational eye movement (FEyeM) mechanism, we propose a multi-scale contour detection model based on fixational eye movement (MsFem) and the surrounding suppression mechanism. A bank of filters was proposed to simulate the influence of FEyeMs on nCRF, and multi-scale cues were utilized to improve the fine and coarse contour extraction and texture inhibition. The experiments showed that MsFem outperformed some biologically motivated ones in retaining the small-scale target contour information and suppressing the large-scale background textures.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities. Elsevier, Amsterdam (2004)

    Google Scholar 

  2. Milan, S., Roger, B., Vaclav, H.: Image processing, analysis, and machine vision. J. Electron. Imaging 9(82), 685–686 (2014)

    Google Scholar 

  3. Baglodi, V.: Edge detection comparison study and discussion of a new methodology. In: Southeastcon, Southeastcon 09 IEEE (2009)

  4. Dong, H.L., Jang, S.J.: Comparison of two-sample tests for edge detection in noisy images. J. R. Stat. Soc. 51(1), 21–30 (2002)

    Article  MathSciNet  Google Scholar 

  5. Nouri, F., Kazemi, K., Danyali, H.: Salient object detection using local, global and high contrast graphs. Signal Image Video Process. 12(4), 659–667 (2018)

    Article  Google Scholar 

  6. Yadollahi, M., Procházka, A., Kašparová, M., Vyšata, O.: The use of combined illumination in segmentation of orthodontic bodies. Signal Image Video Process. 9(1), 1–8 (2014)

    Google Scholar 

  7. Tang, Q., Sang, N., Zhang, T.: Extraction of salient contours from cluttered scenes. Pattern Recognit. 40(11), 3100–3109 (2007)

    Article  Google Scholar 

  8. Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Trans. Image Process. 12(7), 729–739 (2003)

    Article  Google Scholar 

  9. Nong, S., Li, H., Peng, W., Zhang, T.: Knowledge-based adaptive thresholding segmentation of digital subtraction angiography images. Image Vis. Comput. 25(8), 1263–1270 (2007)

    Article  Google Scholar 

  10. Nong, S., Qiling, T., Tianxu, Z.: Contour detection based on inhibition of primary visual cortex. J. Infrared Millim. Waves 26(1), 47–51 (2007)

    Google Scholar 

  11. Huang, W., Jiao, L., Jia, J., Yu, H.: A neural contextual model for detecting perceptually salient contours. Pattern Recognit. Lett. 30(11), 985–993 (2009)

    Article  Google Scholar 

  12. Yang, K., Li, Y.: A coutour detection model based on surround inhibition with multiple cues. In: Chinese Conference on Pattern Recognition (2012)

  13. Jing, H., Jiang, Y., Yi, Z., Bai, L.F.: Salient contour extraction from complex natural scene in night vision image. Infrared Phys. Technol. 63(11), 165–177 (2014)

    Google Scholar 

  14. Xiao, J., Cai, C.: Contour detection based on horizontal interactions in primary visual cortex. Electron. Lett. 50(5), 359–361 (2014)

    Article  Google Scholar 

  15. Yang, K., Gao, S., Li, C., Li, Y.: Efficient color boundary detection with color-opponent mechanisms. In: Computer Vision & Pattern Recognition (2013)

  16. Yang, K., Gao, S., Guo, C., Li, C., Li, Y.: Boundary detection using double-opponency and spatial sparseness constraint. IEEE Trans. Image Process. 24(8), 2565–2578 (2015)

    Article  MathSciNet  Google Scholar 

  17. Akbarinia, A., Parraga, C.A.: Biologically-inspired edge detection through surround modulation. In: Proceedings of the British Machine Vision Conference, pp. 1–13 (2016)

  18. Costela, F.M., McCamy, M.B., Macknik, S.L., Otero-Millan, J., Martinez-Conde, S.: Microsaccades restore the visibility of minute foveal targets. PeerJ 1, e119 (2013)

    Article  Google Scholar 

  19. Sui, X., Hang, G., Sun, Y., Qian, C., Gu, G.: Infrared super-resolution imaging method based on retina micro-motion. Infrared Phys. Technol. 60(5), 340–345 (2013)

    Article  Google Scholar 

  20. Martinez-Conde, S., Otero-Millan, J., Macknik, S.L.: The impact of microsaccades on vision: towards a unified theory of saccadic function. Nat. Rev. Neurosci. 14(2), 83–96 (2013)

    Article  Google Scholar 

  21. Wei, D.: Image super-resolution reconstruction using the high-order derivative interpolation associated with fractional filter functions. IET Signal Proc. 10(9), 1052–1061 (2017)

    Article  Google Scholar 

  22. Wei, D., Li, Y.M.: Generalized Sampling Expansions with Multiple Sampling Rates for Lowpass and Bandpass Signals in the Fractional Fourier Transform Domain. IEEE Trans. Signal Process. 64(18), 4861–4874 (2016)

    Article  MathSciNet  Google Scholar 

  23. Wei, D., Li, Y.: Reconstruction of multidimensional bandlimited signals from multichannel samples in linear canonical transform domain. Signal Process. IET 8(6), 647–657 (2014)

    Article  Google Scholar 

  24. Zeng, C., Li, Y., Yang, K., Li, C.: Contour detection based on a non-classical receptive field model with butterfly-shaped inhibition subregions. Neurocomputing 74(10), 1527–1534 (2011)

    Article  Google Scholar 

  25. Zeng, C., Li, Y., Li, C.: Center–surround interaction with adaptive inhibition: a computational model for contour detection. NeuroImage 55(1), 49–66 (2011)

    Article  Google Scholar 

  26. Wei, H., Lang, B., Zuo, Q.: Contour detection model with multi-scale integration based on non-classical receptive field. Neurocomputing 103, 247–262 (2013)

    Article  Google Scholar 

  27. Yang, K.-F., Li, C.-Y., Li, Y.-J.: Multifeature-based surround inhibition improves contour detection in natural images. IEEE Trans. Image Process. 23(12), 5020–5032 (2014)

    Article  MathSciNet  Google Scholar 

  28. Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision, pp. 354–370. Springer (2016)

  29. Martinez-Conde, S., Macknik, S.L., Hubel, D.H.: The role of fixational eye movements in visual perception. Nat. Rev. Neurosci. 5(3), 229–240 (2004)

    Article  Google Scholar 

Download references

Acknowledgements

The authors appreciate the anonymous reviewers for their helpful and constructive comments on an earlier draft of this paper. This work was supported by the National Natural Science Foundation of China (Grant No. 61866002), the Guangxi Natural Science Foundation (Grant Nos. 2018GXNSFAA138122 and 2015GXNSFAA139293), the Innovation Project of Guangxi Graduate Education (Grant No. YCSW2018203), and the Innovation Project of GuangXi University of Science and Technology Graduate Education (Grant Nos. GKYC201706 and GKYC201803). The funders had no role in the study design the collection, analysis, interpretation of data, writing of the report, or the decision to submit the article for publication.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuan Lin.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, C., Zhang, Q. & Cao, Y. Multi-scale contour detection model based on fixational eye movement mechanism. SIViP 14, 57–65 (2020). https://doi.org/10.1007/s11760-019-01524-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01524-2

Keywords

Navigation