Skip to main content
Log in

Joint Contours, Corner and T-Junction Detection: An Approach Inspired by the Mammal Visual System

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

We introduce a new algorithm that allows the detection of line segments, contours, corners and T-junctions. The proposed model is inspired by the mammal visual system. The detection of corners and T-junctions plays a role as part of the process in contour detection. This method unifies tasks that have been traditionally worked apart. An a-contrario validation is applied to select the most meaningful contours without the need of fixing any critical parameter.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Aggarwal, N., Karl, W.C.: Line detection in images through regularized hough transform. IEEE Trans. Image Process. 15(3), 582–591 (2006)

    Article  Google Scholar 

  2. Arbelaez, 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 

  3. Awrangjeb, M., Lu, G.: Robust image corner detection based on the chord-to-point distance accumulation technique. IEEE Trans. Multimedia 10(6), 1059–1072 (2008)

    Article  Google Scholar 

  4. Bell, A.J., Sejnowski, T.J.: The independent components of natural scenes are edge filters. Vis. Res. 37(23), 3327–3338 (1997)

    Article  Google Scholar 

  5. Ben-Shahar, O., Huggins, P.S., Izo, T., Zucker, S.W.: Cortical connections and early visual function: intra-and inter-columnar processing. J. Physiol. Paris 97(2), 191–208 (2003)

    Article  Google Scholar 

  6. Ben-Shahar, O., Zucker, S.: Geometrical computations explain projection patterns of long-range horizontal connections in visual cortex. Neural Comput. 16(3), 445–476 (2004)

    Article  MATH  Google Scholar 

  7. Bowyer, K., Kranenburg, C., Dougherty, S.: Edge detector evaluation using empirical roc curves. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 354–359. IEEE (1999)

  8. Burns, J.B., Hanson, A.R., Riseman, E.M.: Extracting straight lines. IEEE Trans. Pattern Anal. Mach. Intell. 4, 425–455 (1986)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Cao, F.: Application of the gestalt principles to the detection of good continuations and corners in image level lines. Comput. Vis. Sci. 7(1), 3–13 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  11. Cardelino, J., Caselles, V., Bertalmío, M., Randall, G.: A contrario hierarchical image segmentation. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 4041–4044. IEEE (2009)

  12. Caselles, V., Coll, B., Morel, J.M.: A Kanizsa programme. In: Serapioni, R., Tomarelli, F. (eds.) Serapioni, R., Tomarelli, F. (eds.) Variational Methods for Discontinuous Structures, pp. 35–55. Birkhäuser, Basel (1996)

  13. Desolneux, A., Moisan, L., Morel, J.-M.: Meaningful alignments. Int. J. Comput. Vis. 40(1), 7–23 (2000)

    Article  MATH  Google Scholar 

  14. Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by helmholtz principle. J. Math. Imaging Vis. 14(3), 271–284 (2001)

    Article  MATH  Google Scholar 

  15. Desolneux, A., Moisan, L., Morel, J.-M.: From Gestalt Theory to Image Analysis, a Probabilistic Approach. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  16. Dickscheid, T., Schindler, F., Förstner, W.: Coding images with local features. Int. J. Comput. Vis. 94(2), 154–174 (2011)

    Article  MATH  Google Scholar 

  17. Field, D.J., Hayes, A., Hess, R.F.: Contour integration by the human visual system: evidence for a local “association field”. Vis. Res. 33(2), 173–193 (1993)

    Article  Google Scholar 

  18. Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)

    Article  Google Scholar 

  19. Galamhos, C., Matas, J., Kittler, J.: Progressive probabilistic hough transform for line detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 554–560. IEEE (1999)

  20. Gordon, A., Glazko, G., Qiu, X., Yakovlev, A.: Control of the mean number of false discoveries, Bonferroni and stability of multiple testing. Ann. Appl. Stat. 1(1), 179–190 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  21. Grompone von Gioi, R., Jakubowicz, J., Morel, J.-M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010)

    Article  Google Scholar 

  22. Grompone von Gioi, R., Jakubowicz, J., Morel, J.-M., Randall, G.: LSD: a line segment detector. Image Process. On Line 2(3), 35–55 (2012)

    Article  Google Scholar 

  23. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, pp. 10–5244. Citeseer (1988)

  24. Hochberg, Y., Tamhane, A.C.: Multiple Comparison Procedures. Wiley, New York (1987)

    Book  MATH  Google Scholar 

  25. Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol 148(3), 574–591 (1959)

    Article  Google Scholar 

  26. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)

    Article  Google Scholar 

  27. Ishikawa, H., Geiger, D.: Segmentation by grouping junctions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Proceedings, pp. 125–131. IEEE (1998)

  28. Kanizsa, G.: Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, Santa Barbara (1979)

    Google Scholar 

  29. Kenney, C.S., Zuliani, M., Manjunath, B.S.: An axiomatic approach to corner detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 191–197. IEEE (2005)

  30. Köthe, U.: Edge and junction detection with an improved structure tensor. In: Michaelis, B., Krell, G. (eds.) Joint Pattern Recognition Symposium, pp. 25–32. Springer, Berlin, Heidelberg (2003)

    Chapter  Google Scholar 

  31. Lin, L., Peng, S., Porway, J., Zhu, S.C., Wang, Y.: An empirical study of object category recognition: sequential testing with generalized samples. In: IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (2007)

  32. Lisani, J.L., Buades, A., Morel, J.-M.: How to explore the patch space. Inverse Probl. Imaging 7(3), 813–838 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  33. Maire, M., Arbeláez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)

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

    Article  Google Scholar 

  35. Mokhtarian, F., Suomela, R.: Robust image corner detection through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1376–1381 (1998)

    Article  Google Scholar 

  36. Morel, J.M., Salembier, P.: Monocular depth by nonlinear diffusion. In: Sixth Indian Conference on Computer Vision, Graphics & Image Processing. ICVGIP’08, pp. 95–102. IEEE (2008)

  37. Nieto, M., Cuevas, C., Salgado, L., García, N.: Line segment detection using weighted mean shift procedures on a 2d slice sampling strategy. Pattern Anal. Appl. 14(2), 149–163 (2011)

    Article  MathSciNet  Google Scholar 

  38. Palmer, S.E.: Vision Science: Photons to Phenomenology. The MIT Press, Cambridge (1999)

    Google Scholar 

  39. Pătrăucean, V., Gurdjos, P., Grompone von Gioi, R.: A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In: Computer Vision—ECCV 2012, pp. 572–585. Springer (2012)

  40. Perona, P., Malik, J.: Detecting and localizing edges composed of steps, peaks and roofs. In: Third International Conference on Computer Vision. Proceedings, pp. 52–57. IEEE (1990)

  41. Püspöki, Z., Uhlmann, V., Vonesch, C., Unser, M.: Design of steerable wavelets to detect multifold junctions. IEEE Trans. Image Process. 25(2), 643–657 (2016)

    Article  MathSciNet  Google Scholar 

  42. Püspöki, Z., Unser, M.: Template-free wavelet-based detection of local symmetries. IEEE Trans. Image Process. 24(10), 3009–3018 (2015)

    Article  MathSciNet  Google Scholar 

  43. Rakesh, R.R., Chaudhuri, P., Murthy, C.A.: Thresholding in edge detection: a statistical approach. IEEE Trans. Image Process. 13(7), 927–936 (2004)

    Article  Google Scholar 

  44. Sanguinetti, G., Citti, G., Sarti, A.: Implementation of a model for perceptual completion in r 2\(\times \) s 1. In: Ranchordas, A.K., Araújo, H.J., Pereira, J.M., Braz, J. (eds.) Computer Vision and Computer Graphics. Theory and Applications, pp. 188–201. Springer, Berlin, Heidelberg (2009)

  45. Sarti, A., Citti, G., Manfredini, M.: From neural oscillations to variational problems in the visual cortex. J. Physiol. Paris 97(2), 379–385 (2003)

    Article  Google Scholar 

  46. Shui, P.-L., Zhang, W.-C.: Noise-robust edge detector combining isotropic and anisotropic gaussian kernels. Pattern Recognit. 45(2), 806–820 (2012)

    Article  MATH  Google Scholar 

  47. Shui, P.-L., Zhang, W.-C.: Corner detection and classification using anisotropic directional derivative representations. IEEE Trans. Image Process. 22(8), 3204–3218 (2013)

    Article  Google Scholar 

  48. Xia, G.-S., Delon, J., Gousseau, Y.: Accurate junction detection and characterization in natural images. Int. J. Comput. Vis. 106(1), 31–56 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  49. Zhang, W.-C., Shui, P.-L.: Contour-based corner detection via angle difference of principal directions of anisotropic gaussian directional derivatives. Pattern Recognit. 48(9), 2785–2797 (2015)

    Article  Google Scholar 

  50. Zhang, W.-C., Zhao, Y.-L., Breckon, T.P., Chen, L.: Noise robust image edge detection based upon the automatic anisotropic gaussian kernels. Pattern Recognit. 63, 193–205 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank G. Xia, J. Delon and Y. Gousseau for their code for junction detection. The authors would like to thank N. Oliver for proofreading the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antonio Buades.

Additional information

This work was partially financed by Ministerio de Economia y Competitividad under Grant TIN2014-53772-R.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buades, A., Grompone von Gioi, R. & Navarro, J. Joint Contours, Corner and T-Junction Detection: An Approach Inspired by the Mammal Visual System. J Math Imaging Vis 60, 341–354 (2018). https://doi.org/10.1007/s10851-017-0763-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-017-0763-z

Keywords

Navigation