Skip to main content

Advertisement

Log in

Contour detection based on the interactive response and fusion model of bilateral attention pathways

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

Abstract

Contour detection is the key pre-processing step for human vision to perceive objects. Considering the shunt transmission and interactive response characteristics of visual information in visual pathways, a new contour detection method, which is based on the interactive response and fusion model of bilateral attention pathways, is proposed. Firstly, according to the color antagonism mechanism of single-opponent receptive field (SORF) in the sub-visual cortex, a SORF dynamical adjustment model based on local luminance information was devised to realize the joint coding of luminance and color boundaries. Secondly, a multi-directional fretting method in the optimal azimuth interval was designed by simulating the direction-sensitive characteristics of the classical receptive field of the primary visual cortex. Then, a visual information interaction model based on bilateral attention pathways—which combines visual attention and bilateral shunt mechanisms of visual information—was developed to obtain spatial salient contours of dorsal attention pathway and sparse responses of ventral neurons. Finally, the multi-pathway information fusion mechanism of the high-level visual cortex was introduced, and visual information differences between bilateral attention pathways were used to obtain the target contours. The experimental results show that our method can effectively highlight contours and eliminate textures, which will provide new ideas for subsequent image understanding and analysis.

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. Trivedi, D.N., Shah, N.D., Kothari, A.M., Thanki, R.M.: Analytical Study of Edge Detection Algorithms and Contouring Algorithm. Dental Image Processing for Human Identification, pp. 29–40 (2019)

  2. McIlhagga, W.: The canny edge detector revisited. Int. J. Comput. Vision 91(3), 251–261 (2011)

    Article  MathSciNet  Google Scholar 

  3. Cao, Y.J., Lin, C., Pan, Y.J., Zhao, H.J.: Application of the center–surround mechanism to contour detection. Multimedia Tools Appl. 78, 25121–25141 (2019)

    Article  Google Scholar 

  4. Lin, C., Zhang, Q., Cao, Y.: Multi-scale contour detection model based on fixational eye movement mechanism Signal Image Video Process. 14(1), 57–65 (2020)

    Google Scholar 

  5. Yang, K.F., Gao, S.B., Li, C.Y., Li, Y.: Efficient color boundary detection with color-opponent mechanisms. In: 2013 IEEE Computer Vision and Pattern Recognition, pp. 2810-2817 (2013)

  6. Yang, K.F., Li, C.Y., Li, Y.J.: Boundary detection using double-opponency and spatial sparseness constraint. IEEE Trans. Image Process. 24, 2565–2578 (2015)

    Article  MathSciNet  Google Scholar 

  7. Akbarinia, A., Parraga, C.A.: Feedback and surround modulated boundary detection. Int. J. Computer V. 126, 1367–1380 (2018)

    Google Scholar 

  8. Fang, T., Fan, Y., Wu, W.: Salient contour detection on the basis of the mechanism of bilateral asymmetric receptive fields. SIViP 14, 1461–1469 (2020)

    Article  Google Scholar 

  9. Zhang, Q., Lin, C., Li, F.: Application of binocular disparity and receptive field dynamics: a biologically-inspired model for contour detection. Pattern Recognit. 110, 107657 (2021)

    Article  Google Scholar 

  10. Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194–203 (2001)

    Article  Google Scholar 

  11. Garrigan, P., Hamilton, C.M.: Coherence of visual representations: Attention and integration of contour shape information. Atten. Percept. Psychophys. 76, 2346–2359 (2014)

    Article  Google Scholar 

  12. Li, F., Lin, C., Qing, Z., Wang, R.: A biologically inspired contour detection model based on multiple visual channels and multi-hierarchical visual information. IEEE Access 8, 15410–15422 (2020)

    Article  Google Scholar 

  13. Solomon, S.G., Lennie, P.: The machinery of color vision. Nat. Rev. Neurosci. 8(4), 276–286 (2007)

    Article  Google Scholar 

  14. Lin, C., Zhao, H., Cao, Y.J.: Improved color opponent contour detection model based on dark and light adaptation. Autom. Control. Comput. Sci. 53, 560–571 (2019)

    Article  Google Scholar 

  15. Li, S., Xu, Y., Cong, W., Ma, S., Zhu, M., Qi, M.: Biologically inspired hierarchical contour detection with surround modulation and neural connection. Sensors 18(8), 2559 (2018)

    Article  Google Scholar 

  16. Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: an information theoretic approach. J. Vision 9(3), 1–24 (2009)

    Article  Google Scholar 

  17. Cloutman, L.L.: Interaction between dorsal and ventral processing streams: Where, when and how? Brain Lang. 127(2), 251–263 (2013)

    Article  Google Scholar 

  18. Madary, M.: The dorsal stream and the visual horizon. Phenomenol. Cogn. Sci. 10, 423 (2011)

    Article  Google Scholar 

  19. Alpert, S., Galun, M., Brandt, A., Basri, R.: Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 315–327 (2011)

    Article  Google Scholar 

  20. Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  21. Wang, Y.P., Zhao, X., Yin, L., Huang, K.Q.: Deep crisp boundaries: from boundaries to higher-level tasks. IEEE Trans. Image Process. 28(3), 1285–1298 (2019)

    Article  MathSciNet  Google Scholar 

  22. Al-Amaren, A., Ahmad, M.O., Swamy, M.N.S.: RHN: a residual holistic neural network for edge detection. IEEE Access. 9, 74646–74658 (2021)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yingle Fan.

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

Xu, Y., Fan, Y. Contour detection based on the interactive response and fusion model of bilateral attention pathways. SIViP 16, 1379–1387 (2022). https://doi.org/10.1007/s11760-021-02090-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-02090-2

Keywords

Navigation