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Journal of Mathematical Imaging and Vision

, Volume 60, Issue 3, pp 341–354 | Cite as

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

  • Antonio Buades
  • Rafael Grompone von Gioi
  • Julia Navarro
Article

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.

Keywords

Contour detection Line segment detection Visual system A-contrario validation 

Notes

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.

References

  1. 1.
    Aggarwal, N., Karl, W.C.: Line detection in images through regularized hough transform. IEEE Trans. Image Process. 15(3), 582–591 (2006)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Bell, A.J., Sejnowski, T.J.: The independent components of natural scenes are edge filters. Vis. Res. 37(23), 3327–3338 (1997)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefzbMATHGoogle Scholar
  7. 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)Google Scholar
  8. 8.
    Burns, J.B., Hanson, A.R., Riseman, E.M.: Extracting straight lines. IEEE Trans. Pattern Anal. Mach. Intell. 4, 425–455 (1986)CrossRefGoogle Scholar
  9. 9.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)CrossRefGoogle Scholar
  10. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 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)Google Scholar
  12. 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)Google Scholar
  13. 13.
    Desolneux, A., Moisan, L., Morel, J.-M.: Meaningful alignments. Int. J. Comput. Vis. 40(1), 7–23 (2000)CrossRefzbMATHGoogle Scholar
  14. 14.
    Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by helmholtz principle. J. Math. Imaging Vis. 14(3), 271–284 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Desolneux, A., Moisan, L., Morel, J.-M.: From Gestalt Theory to Image Analysis, a Probabilistic Approach. Springer, Berlin (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Dickscheid, T., Schindler, F., Förstner, W.: Coding images with local features. Int. J. Comput. Vis. 94(2), 154–174 (2011)CrossRefzbMATHGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)Google Scholar
  20. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, pp. 10–5244. Citeseer (1988)Google Scholar
  24. 24.
    Hochberg, Y., Tamhane, A.C.: Multiple Comparison Procedures. Wiley, New York (1987)CrossRefzbMATHGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)Google Scholar
  28. 28.
    Kanizsa, G.: Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, Santa Barbara (1979)Google Scholar
  29. 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)Google Scholar
  30. 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)CrossRefGoogle Scholar
  31. 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)Google Scholar
  32. 32.
    Lisani, J.L., Buades, A., Morel, J.-M.: How to explore the patch space. Inverse Probl. Imaging 7(3), 813–838 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 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)Google Scholar
  34. 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)CrossRefGoogle Scholar
  35. 35.
    Mokhtarian, F., Suomela, R.: Robust image corner detection through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1376–1381 (1998)CrossRefGoogle Scholar
  36. 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)Google Scholar
  37. 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)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Palmer, S.E.: Vision Science: Photons to Phenomenology. The MIT Press, Cambridge (1999)Google Scholar
  39. 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)Google Scholar
  40. 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)Google Scholar
  41. 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)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Püspöki, Z., Unser, M.: Template-free wavelet-based detection of local symmetries. IEEE Trans. Image Process. 24(10), 3009–3018 (2015)MathSciNetCrossRefGoogle Scholar
  43. 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)CrossRefGoogle Scholar
  44. 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)Google Scholar
  45. 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)CrossRefGoogle Scholar
  46. 46.
    Shui, P.-L., Zhang, W.-C.: Noise-robust edge detector combining isotropic and anisotropic gaussian kernels. Pattern Recognit. 45(2), 806–820 (2012)CrossRefzbMATHGoogle Scholar
  47. 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)CrossRefGoogle Scholar
  48. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Universitat Illes BalearsPalma de MallorcaSpain
  2. 2.CMLA, ENS CachanCachanFrance

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