Skip to main content
SpringerLink
Log in

A contour perception model that simulates the complex connection pattern of the visual cortex

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Contour detection is the basic content of image processing and plays an important role in image analysis and target recognition. This paper proposed a contour perception model that simulates the complex connection pattern of the visual cortex. The connection included the feedforward input from the lateral geniculate body (LGN), the horizontal input from the neurons in the same layer, and the feedback input from the advanced visual cortex. Using the sparse coding characteristics of the LGN, the windmill-like structure receptive field of the primary visual cortex, and the hue perception characteristics of the advanced visual cortex to improve the accuracy of the contour extracted by the proposed model. Choosing the BSDS500 natural scene dataset as the experimental object, the F-score is selected as the evaluation index. The average optimal F-score of the proposed method is 0.72, which is better than other mainstream biological vision-based methods. Concurrently, the NYUD dataset is used for further verification. To comprehensively verify the effectiveness of the model proposed in this paper, Performance-value rather than F-score is selected as the evaluation index. The average optimal Performance-value of the proposed method is 0.42, which shows better results, too. The complex connection pattern allows neural encoding and decoding to make full use of the characteristics of information exchange between the visual cortexes, which is more in line with the biological vision system.

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

Similar content being viewed by others

References

  1. Akbarinia A, Parraga CA (2018) Feedback and surround modulated boundary detection[J]. Int J Comput Vision 126(12):1367–1380

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Arbelaez P, Maire M, Fowlkes C et al (2010) Contour detection and hierarchical image segmentation[J]. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  4. Azzopardi G, Petkov N (2012) A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model[J]. Biol Cybern 106(3):177–189

    Article  Google Scholar 

  5. Bertasius G, Shi J, Torresani L (2015) Deepedge: a multi-scale bifurcated deep network for top-down contour detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, pp 4380–4389

  6. Buchsbaum G, Gottschalk A (1983) Trichromacy, opponent colours coding and optimum colour information transmission in the retina[J]. Proc R Soc Lond Ser B Biol Sci 220(1218):89–113

    Google Scholar 

  7. Canny J (1986) A computational approach to edge detection[J]. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  8. Cao YJ, Lin C, Pan YJ et al (2019) Application of the center–surround mechanism to contour detection[J]. Multimed Tools Appl 78(17):25121–25141

    Article  Google Scholar 

  9. Capparelli F, Pawelzik K, Ernst U (2019) Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics[J]. PLoS Comput Biol 15(10):e1007370

    Article  Google Scholar 

  10. Dollár P, Zitnick CL (2013) Structured forests for fast edge detection[C]. Proceedings of the IEEE international conference on computer vision. IEEE, Sydney, pp 1841–1848

  11. Fang T, Fan Y, Wu W (2020) Salient contour detection on the basis of the mechanism of bilateral asymmetric receptive fields[J]. SIViP 14(7):1461–1469

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex[J]. J Physiol 160(1):106–154

    Article  Google Scholar 

  14. Jacob T, Snyder W, Feng J et al (2016) A neural model for straight line detection in the human visual cortex[J]. Neurocomputing 199:185–196

    Article  Google Scholar 

  15. Li M, Song XM, Xu T et al (2019) Subdomains within orientation columns of primary visual cortex[J]. Sci Adv 5(6):eaaw0807

    Article  Google Scholar 

  16. Lim JJ, Zitnick CL, Dollár P (2013) Sketch tokens: a learned mid-level representation for contour and object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Portland, pp 3158–3165

  17. Lin C, Zhao HJ, Cao YJ (2019) Improved color opponent contour detection model based on dark and light adaptation[J]. Autom Control Comput Sci 53(6):560–571

    Article  Google Scholar 

  18. Liu Y, Cheng MM, Hu X et al (2017) Richer convolutional features for edge detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Hawaii, pp 3000–3009

  19. Liu Z, Zhang W, Zhao P (2020) A cross-modal adaptive gated fusion generative adversarial network for RGB-D salient object detection[J]. Neurocomputing 387:210–220

    Article  Google Scholar 

  20. Liu L, Zhang J, Saad MA et al (2020) Blind S3D image quality prediction using classical and non-classical receptive field models[J]. Sig Process Image Commun 87:115915

    Article  Google Scholar 

  21. Ma W, Gong C, Xu S et al (2020) Multi-scale spatial context-based semantic edge detection[J]. Inf Fusion 64:238–251

    Article  Google Scholar 

  22. Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues[J]. IEEE Trans Pattern Anal Mach Intell 26(5):530–549

    Article  Google Scholar 

  23. Melotti D, Heimbach K, Rodríguez-Sánchez A et al (2020) A robust contour detection operator with combined push-pull inhibition and surround suppression[J]. Inf Sci 524:229–240

    Article  MathSciNet  Google Scholar 

  24. Mingolla E, Ross W, Grossberg S (1999) A neural network for enhancing boundaries and surfaces in synthetic aperture radar images[J]. Neural Netw 12(3):499–511

    Article  Google Scholar 

  25. Mohan YS, Jayakumar J, Lloyd EKJ et al (2019) Diversity of feature selectivity in macaque visual cortex arising from a limited number of broadly tuned input channels[J]. Cereb Cortex 29(12):5255–5268

  26. Moratti S, Méndez-Bértolo C, Del-Pozo F et al (2014) Dynamic gamma frequency feedback coupling between higher and lower order visual cortices underlies perceptual completion in humans[J]. NeuroImage 86:470–479

    Article  Google Scholar 

  27. Murgas KA, Wilson AM, Michael V et al (2020) Unique spatial integration in mouse primary visual cortex and higher visual areas[J]. J Neurosci 40(9):1862–1873

    Article  Google Scholar 

  28. Nathans J, Thomas D, Hogness DS (1986) Molecular genetics of human color vision: the genes encoding blue, green, and red pigments[J]. Science 232(4747):193–202

    Article  Google Scholar 

  29. Palmerston JB, Zhou Y, Chan RHM (2020) Comparing biological and artificial vision systems: network measures of functional connectivity[J]. Neurosci Lett 739:135407

  30. Wang Y, Zhao X, Li Y et al (2018) Deep crisp boundaries: From boundaries to higher-level tasks[J]. IEEE Trans Image Process 28(3):1285–1298

    Article  MathSciNet  MATH  Google Scholar 

  31. Wu R, Feng M, Guan W et al (2019) A mutual learning method for salient object detection with intertwined multi-supervision[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. IEEE, Long Beach, pp 8150–8159

  32. Xie S, Tu Z (2015) Holistically-nested edge detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, pp 1395–1403

  33. Yang KF, Li CY, Li YJ (2014) Multifeature-based surround inhibition improves contour detection in natural images[J]. IEEE Trans Image Process 23(12):5020–5032

    Article  MathSciNet  MATH  Google Scholar 

  34. Yang KF, Gao SB, Guo CF et al (2015) Boundary detection using double-opponency and spatial sparseness constraint[J]. IEEE Trans Image Process 24(8):2565–2578

    Article  MathSciNet  MATH  Google Scholar 

  35. Yang H, Li Y, Yan X et al (2019) ContourGAN: Image contour detection with generative adversarial network[J]. Knowl Based Syst 164:21–28

    Article  Google Scholar 

  36. Yoav B (1988) Opening the Box of a Boxplot. Am Stat 42(4):257. https://doi.org/10.2307/2685133

  37. Yuan B, Han L, Yan H (2021) Explore double-opponency and skin color for saliency detection[J]. Neurocomputing 425:219–230

    Article  Google Scholar 

  38. Zhang H, Sindagi V, Patel VM (2019) Image de-raining using a conditional generative adversarial network[J]. IEEE Trans Circuits Syst Video Technol 30(11):3943–3956

    Article  Google Scholar 

  39. Zhang Y, Tian Y, Kong Y et al (2020) Residual dense network for image restoration[J]. IEEE Trans Pattern Anal Mach Intell 43(7):2480–2495

    Article  Google Scholar 

  40. Zhang Q, Lin C, Li F (2021) Application of binocular disparity and receptive field dynamics: A biologically-inspired model for contour detection[J]. Pattern Recogn 110:107657

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the Laboratory of Pattern Recognition and Image Processing in Hangzhou Dianzi University.

Author information

Authors and Affiliations

  1. Laboratory of Pattern Recognition and Image Processing, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhefei Cai & Yingle Fan

Authors
  1. Zhefei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingle Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingle Fan.

Ethics declarations

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “A contour perception model that simulates the complex connection pattern of the visual cortex”.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, Z., Fan, Y. A contour perception model that simulates the complex connection pattern of the visual cortex. Multimed Tools Appl 82, 19347–19368 (2023). https://doi.org/10.1007/s11042-022-14194-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14194-z

Keywords

Search

Navigation