Contour pixel classification for character skeletonization

  • Maria Frucci
  • Angelo Marcelli
Oral Presentations A. Low Level Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1339)


In this paper it is proposed a mechanism for implementing the isotropic propagation of the figure border to obtain the skeleton of elongated shapes. The mechanism allows for detecting, classifying and labelling the contour pixels depending on the characteristics of the wavefronts which interact during the propagation. The skeleton provided by the algorithm is not affected by the distortions which arise in correspondence of regions where the parts of the figure interact. Moreover, it is given in terms of a set of digital lines, each one corresponding to one of the figure parts, rather than by a connected set of pixels.


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  1. [1]
    H. Blum, “A transformation for extracting new descriptors of shape”, in: Models for the Perception of Speech and Visual Form, (W. Watjen-Dunn, ed.), MIT Press, Cambridge:MA, 1967, pp.362–380.Google Scholar
  2. [2]
    C. Arcelli and G.Sanniti di Baja, “ Skeletons of planar patterns”, in Topological Algorithms for Digital Image Processing, T.Y.Kong and A. Rosenfeld (Editors), 1996 Elsevier Science.Google Scholar
  3. [3]
    F. Leymarie and M.D. Levine, “Simulating the grassfire transform using an active contour model ”, IEEE Trans. Patt. Anal. Mach. Intell., 14, 56–75, 1992.Google Scholar
  4. [4]
    L. Lam, S.W. Lee and C.Y. Suen, “Thinning methodologies-A comprehensive survey”, IEEE Trans. on Patt. Anal and Mach. Intell., vol. PAMI-14, no.9, 1992, pp.869–887.Google Scholar
  5. [5]
    G. Boccignone, A. Chianese, L. P. Cordella and A. Marcelli, “Using Skeletons for OCR”, in Progress in Image Analysis and Processing (L.P. Cordella et al., Eds.), pp. 275–282, World Publisher, SINGAPORE, 1989.Google Scholar
  6. [6]
    S. Lee and J. C. Pan, “Offline tracing and representation of signatures”, IEEE Trans. on Syst., Man and Cybern., SMC-22, 1992, pp. 755–771.Google Scholar
  7. [7]
    S. W. LU and H. Xe, “False stroke detection and elimination for character recognition”, Pattern Recognition Letters, 13, 1992, pp. 745–755.Google Scholar
  8. [8]
    M. Frucci and A. Marcelli, “Line representation of elongated shapes”, in Lecture Notes in Computer Science, V.Hlavàc, R. Sàra Eds., Springer-Verlag, vol. 970, pp. 643–648.Google Scholar
  9. [9]
    M. Frucci and A. Marcelli, “Computing line representations of ribbonlike objects”, Proc. ACCV'95 Second Asian Conf. on Computer Vision, Singapore, December 5–8, 1995, vol. 3, pp.548–553.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Maria Frucci
    • 1
  • Angelo Marcelli
    • 2
  1. 1.Istituto di Cibemetica, CNRArco Felice (NA)Italy
  2. 2.Dipartimento di Informatica e SistemisticaUniversita' di Napoli “Federico II”NapoliItaly

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