Stroke Segmentation in Infrared Reflectograms

  • Paul Kammerer
  • Georg Langs
  • Robert Sablatnig
  • Ernestine Zolda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


An algorithm for the automatic segmentation of strokes in underdrawings — the basic concept of the artist — in ancient panel paintings is presented. The purpose of the stroke analysis is the determination of the drawing tool used to draft the painting. This information allows significant support for a systematic stylistic approach in the analysis of paintings. Up to now, this analysis has been made by naked eye examination only, and the restricted human optical retentiveness complicated the comparison of different underdrawings with respect to drawing tools and stroke characteristics. Stroke segmentation in painting is related to the extraction and recognition of handwritings, therefore similar techniques to segment the strokes from the background incorporating boundary information are used. Following the segmentation, the approximation of the stroke boundary by a closed polygon done based on active contours. Results of the algorithms developed are presented for both test panels and real reflectograms.


Segmentation Algorithm Active Contour Test Panel Search Window Canny Edge Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    J.-M. Chassery W. Puech, A.G. Bors and I. Pitas. Mosaicing of paintings on curved surfaces. In Third IEEE Workshop on Applications of Computer Vision (WACV’96), pages 44–49, 1996.Google Scholar
  2. 2.
    D. Lagunovsky, M. Frucci, and G.S. di Baja. Image processing tools for fresco restoration. In 14th ICPR, volume 3, pages 326–329, 2000.Google Scholar
  3. 3.
    M. Pappas and I. Pitas. Digital color restoration of old paintings. Trans. On Image Processing, 9(2):291–294, 2000.CrossRefGoogle Scholar
  4. 4.
    J.R.J. van Asperen de Boer. Infrared reflectography and computer image processing. New alternatives. In Le Dessin Sousjacent dans la Peinture, Coll. IX, pages 267–273, 1993.Google Scholar
  5. 5.
    S. Kroner and A. Lattner. Authentication of free hand drawings by pattern recognition methods. In 14th ICPR, volume 1, pages 462–464, 1998.Google Scholar
  6. 6.
    Robert Sablatnig, Paul Kammerer, and Ernestine Zolda. Hierarchical classification of painted portraits using face-and brush stroke models. In 14th ICPR, Brisbane, Australia, August 17–20, 1998.Google Scholar
  7. 7.
    S. Tanaka, J. Kurumizawa, S. Inokuchi, and Y. Iwadate. Composition analyszer: support tool for composition analysis on paintin masterpieces. Knowledge-Based Systems, 13:459–470, 2000.CrossRefGoogle Scholar
  8. 8.
    H. J. van den Herik and E. O. Postma. Future Directions for Intelligent Systems and Information Sciences, chapter Part II, Discovering the visual signature of painters. Springer, 2000.Google Scholar
  9. 9.
    J.R. Mansfield, M.G. Sowa, C. Majzels, C. Collins, E. Cloutis, and H.H. Mantsch. Near infrared spectroscopic reflectance imaging: supervised vs. unsupervised analysis using an art conservation application. Vibrational Spectroscopy, 19:33–45, 1999.CrossRefGoogle Scholar
  10. 10.
    J.R.J. Van Asperen de Boer. Infrared Reflectography. — A Contribution to the Examination of Earlier European Paintings. PhD thesis, Univ. Amsterdam, 1970.Google Scholar
  11. 11.
    D.S. Doermann and A. Rosenfeld. Recovery of temporal information from static images of handwriting. International Journal of Computer Vision, 52(1–2):143–164, 1994.Google Scholar
  12. 12.
    R. Plamondon and S.N. Srihari. On-line and off-line handwriting recognition: A comprehensive survey. Trans. on Pattern Analysis and Machine Intelligence, 22(1):63–84, 2000.CrossRefGoogle Scholar
  13. 13.
    Chenyang Xu and Jerry L. Prince. Snakes, shapes and gradient vector flow. IEEE Transactions on image Processing, 7(3):359–369, March 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    G. Langs, H. Bischof, and P.L. Peloschek. Automatic quantification of destructive changes caused by rheumatoid arthritis. Technical Report 79, Vienna University of Technology, Pattern Recognition and Image Processing Group, 2003.Google Scholar
  15. 15.
    J. Serra. Les treillis visqueux. Technical Report N-51/99/MM, CMM, Ecole des Mines de Paris, 1999.Google Scholar
  16. 16.
    A. Hanbury, P. Kammerer, and E. Zolda. Painting crack elimination using viscous morphological reconstruction. appears in 12th Intl. Conf. on Image Analysis and Processing, ICIAP2003.Google Scholar
  17. 17.
    E. L’Homer. Extraction of strokes in handwritten characters. Pattern Recognition, 33(7):1147–1160, 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Paul Kammerer
    • 1
  • Georg Langs
    • 1
  • Robert Sablatnig
    • 1
  • Ernestine Zolda
    • 1
  1. 1.PRIP - Vienna University of TechnologyViennaAustria

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