An Improved Directional Convexity Measure for Binary Images | SpringerLink
Skip to main content
Springer Nature Link
Log in

An Improved Directional Convexity Measure for Binary Images

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

Included in the following conference series:

Abstract

Balázs et al. (Fundamenta Informaticae 141:151–167, 2015) proposed a measure of directional convexity of binary images based on the geometric definition of shape convexity. The measure is useful for various applications of digital image processing and pattern recognition, especially in binary tomography. Here we provide an improvement of this measure making it to follow better the intuitive concept of geometric convexity and to be more suitable to distinguish between thick and thin objects.

The research was supported by the NKFIH OTKA [grant number K112998].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Balázs, P., Brunetti, S.: A measure of Q-convexity. In: Normand, N., Guédon, J., Autrusseau, F. (eds.) DGCI 2016. LNCS, vol. 9647, pp. 219–230. Springer, Cham (2016). doi:10.1007/978-3-319-32360-2_17

    Chapter  Google Scholar 

  2. Balázs, P., Ozsvár, Z., Tasi, T., Nyúl, L.: A measure of directional convexity inspired by binary tomography. Fundam. Informaticae 141(2–3), 151–167 (2015)

    Article  MathSciNet  Google Scholar 

  3. Barcucci, E., Del Lungo, A., Nivat, M., Pinzani, R.: Medians of polyominoes: a property for reconstruction. Int. J. Imaging Syst. Technol. 9(2–3), 69–77 (1998)

    Article  Google Scholar 

  4. Boxer, L.: Computing deviations from convexity in polygons. Pattern Recognit. Lett. 14(3), 163–167 (1993)

    Article  Google Scholar 

  5. Brunetti, S., Lungo, A.D., Ristoro, F.D., Kuba, A., Nivat, M.: Reconstruction of 4- and 8-connected convex discrete sets from row and column projections. Linear Algebra Appl. 339(1), 37–57 (2001)

    Article  MathSciNet  Google Scholar 

  6. Chrobak, M., Durr, C.: Reconstructing hv-convex polyominoes from orthogonal projections. Inf. Process. Lett. 69(6), 283–289 (1999)

    Article  MathSciNet  Google Scholar 

  7. Gorelick, L., Veksler, O., Boykov, Y., Nieuwenhuis, C.: Convexity shape prior for segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 675–690. Springer, Cham (2014). doi:10.1007/978-3-319-10602-1_44

    Chapter  Google Scholar 

  8. Gorelick, L., Veksler, O., Boykov, Y., Nieuwenhuis, C.: Convexity shape prior for binary segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 258–271 (2017)

    Article  Google Scholar 

  9. Herman, G.T., Kuba, A.: Advances in Discrete Tomography and Its Applications. Applied and Numerical Harmonic Analysis. Birkhauser, Baserl (2007)

    Book  Google Scholar 

  10. Latecki, L.J., Lakamper, R.: Convexity rule for shape decomposition based on discrete contour evolution. Comput. Vis. Image Underst. 73(3), 441–454 (1999)

    Article  Google Scholar 

  11. Rahtu, E., Salo, M., Heikkila, J.: A new convexity measure based on a probabilistic interpretation of images. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1501–1512 (2006)

    Article  Google Scholar 

  12. Rosin, P.L., Žunić, J.: Probabilistic convexity measure. IET Image Process. 1(2), 182–188 (2007)

    Article  Google Scholar 

  13. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, Boston (2014)

    Google Scholar 

  14. Stern, H.I.: Polygonal entropy: a convexity measure. Pattern Recognit. Lett. 10(4), 229–235 (1989)

    Article  Google Scholar 

  15. Tasi, T.S., Nyúl, L.G., Balázs, P.: Directional convexity measure for binary tomography. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 9–16. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41827-3_2

    Chapter  Google Scholar 

  16. Zunic, J., Rosin, P.L.: A new convexity measure for polygons. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 923–934 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Image Processing and Computer Graphics, University of Szeged, Árpád tér 2, Szeged, H6720, Hungary

    Péter Bodnár & Péter Balázs

Authors
  1. Péter Bodnár
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Péter Balázs
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Péter Bodnár .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, QC, Canada

    Farida Cheriet

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bodnár, P., Balázs, P. (2017). An Improved Directional Convexity Measure for Binary Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59876-5_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation