Skip to main content

Advertisement

SpringerLink
Log in

Application of the center–surround mechanism to contour detection

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

Abstract

Physiological studies have revealed that the center–surround mechanism widely exists in the primary stages of the human visual system, such as the retina, lateral geniculate nucleus (LGN), and primary visual cortex (V1). In retina ganglion cells (RGC) and the LGN, the mechanism is well known to have two types: center “on” and center “off.” However, this mechanism in V1 is shown as classical receptive field (CRF) stimulation and surrounding non-CRF suppression. Although these two manifestations differ in function and appear in different areas of the visual pathway, from the perspective of computational simulation, they simply compute the differences between the center and its surrounding information. In the past decade, many bio-inspired computational models have demonstrated that the center–surround mechanism is good at extracting salient contours while suppressing textures. Based on this mechanism, we propose a method for extracting local center–surround contrast information from nature images by using a normalized difference of Gaussian (DoG) function and a sigmoid activated function. Compared with previous contour detection models (especially bio-motivated ones), the proposed method can efficiently suppress textures more quickly and accurately. More importantly, the proposed algorithm yields even better contour detection, yet the computational complexity is similar to the classical Canny operator.

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

Similar content being viewed by others

References

  1. Allman J, Miezin F, McGuinness E (1985) Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. Annu Rev Neurosci 8(1):407–430

    Article  Google Scholar 

  2. Aràndiga F, Cohen A, Donat R, Matei B (2010) Edge detection insensitive to changes of illumination in the image. Image Vis Comput 28(4):553–562

    Article  Google Scholar 

  3. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. 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]. Biological cybernetics, 2012, 106(3): 177-189.

  5. Azzopardi G, Rodriguez-Sanchez A, Piater J, Petkov N (2014) A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection. PLoS One 9(7):e98424

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Chao-Yi L, Wu L (1994) Extensive integration field beyond the classical receptive field of cat's striate cortical neurons—classification and tuning properties. Vis Res 34(18):2337–2355

    Article  Google Scholar 

  8. Coen-Cagli R, Dayan P, Schwartz O (2012) Cortical surround interactions and perceptual salience via natural scene statistics. PLoS Comput Biol 8(3):e1002405

    Article  Google Scholar 

  9. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  10. Cour T, Benezit F, Shi J (2005) Spectral segmentation with multiscale graph decomposition. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol 2. IEEE, pp 1124–1131

  11. Dollar P, Tu Z, Belongie S (2006) Supervised learning of edges and object boundaries. In: CVPR, vol 2. IEEE, pp 1964–1971

  12. Fitzpatrick D (2000) Seeing beyond the receptive field in primary visual cortex. Curr Opin Neurobiol 10(4):438–443

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat's striate cortex. J Physiol 148(3):574–591

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Jones H, Grieve K, Wang W, Sillito A (2001) Surround suppression in primate V1. J Neurophysiol 86(4):2011–2028

    Article  Google Scholar 

  17. Kapadia MK, Westheimer G, Gilbert CD (2000) Spatial distribution of contextual interactions in primary visual cortex and in visual perception. J Neurophysiol 84(4):2048–2062

    Article  Google Scholar 

  18. Konishi S, Yuille AL, Coughlan JM, Zhu SC (2003) Statistical edge detection: learning and evaluating edge cues. IEEE Trans Pattern Anal Mach Intell 25(1):57–74

    Article  Google Scholar 

  19. Kovesi P (1999) Image features from phase congruency. Journal of computer vision research. Videre: J. Comp. Vis. Res1(3):1–26

  20. Li C-Y (1996) Integration fields beyond the classical receptive field: organization and functional properties. Physiology 11(4):181–186

    Article  MathSciNet  Google Scholar 

  21. Lin C, Xu G, Cao Y, Liang C, Li Y (2016) Improved contour detection model with spatial summation properties based on nonclassical receptive field. J. Electron. Imaging 25(4):043018–043018

    Article  Google Scholar 

  22. Lin C, Xu G, Cao Y (2018) Contour detection model using linear and non-linear modulation based on non-CRF suppression[J]. IET Image Processing, 12(6): 993-1003.

  23. Lin C, Xu G, Cao Y (2018) Contour detection model based on neuron behaviour in primary visual cortex[J]. IET Computer Vision, 12(6): 863-872.

  24. Lindgren JT, Hurri J, Hyvärinen A (2008) Spatial dependencies between local luminance and contrast in natural images. J Vis 8(12):6–6

    Article  Google Scholar 

  25. Liu Y, Cheng M-M, Hu X, Wang K, Bai X (2017) Richer convolutional features for edge detection. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 5872–5881

  26. Mairal J, Leordeanu M, Bach F, Hebert M, Ponce J (2008) Discriminative sparse image models for class-specific edge detection and image interpretation. In: European Conference on Computer Vision. Springer, pp 43–56

  27. Mante V, Frazor RA, Bonin V, Geisler WS, Carandini M (2005) Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8(12):1690

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Morrone MC, Owens RA (1987) Feature detection from local energy. Pattern Recogn Lett 6(5):303–313

    Article  Google Scholar 

  30. Papari G, Petkov N (2011) Edge and line oriented contour detection: state of the art. Image Vis Comput 29(2):79–103

    Article  Google Scholar 

  31. Papari G, Petkov N (2011) An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection. Pattern Recogn 44(9):1999–2007

    Article  Google Scholar 

  32. Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  33. Ren X (2008) Multi-scale improves boundary detection in natural images. In: ECCV. Springer, pp 533–545

  34. Spratling MW (2013) Image segmentation using a sparse coding model of cortical area V1. IEEE Trans Image Process 22(4):1631–1643

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  36. Tang Q, Sang N, Liu H (2016) Contrast-dependent surround suppression models for contour detection. Pattern Recogn 60:51–61

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  42. Yang K-F, Li C-Y, Li Y-J (2015) Potential roles of the interaction between model V1 neurons with orientation-selective and non-selective surround inhibition in contour detection. Front. Neural Circuits, 9, pp. 30

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Zhang X-S, Gao S-B, Li R-X, Du X-Y, Li C-Y, Li Y-J (2016) A retinal mechanism inspired color constancy model. IEEE Trans Image Process 25(3):1219–1232

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. College of Electric and Information Engineering, Guangxi University of Science and Technology, Donghuan Road 268, Liuzhou, Guangxi, China

    Yi-Jun Cao, Chuan Lin, Yi-Jian Pan & Hao-Jun Zhao

Authors
  1. Yi-Jun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi-Jian Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao-Jun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuan Lin.

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

Cao, YJ., Lin, C., Pan, YJ. et al. Application of the center–surround mechanism to contour detection. Multimed Tools Appl 78, 25121–25141 (2019). https://doi.org/10.1007/s11042-019-7722-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7722-1

Keywords

Search

Navigation