BINARY MORPHOLOGY AND RELATED OPERATIONS ON RUN-LENGTH REPRESENTATIONS

Thomas M. Breuel

Abstract

Binary morphology on large images is compute intensive, in particular for large structuring elements. Runlength encoding is a compact and space-saving technique for representing images. This paper describes how to implement binary morphology directly on run-length encoded binary images for rectangular structuring elements. In addition, it describes efficient algorithm for transposing and rotating run-length encoded images. The paper evaluates and compares run length morphologial processing on page images from the UW3 database with an efficient and mature bit blit-based implementation and shows that the run length approach is several times faster than bit blit-based implementations for large images and masks. The experiments also show that complexity decreases for larger mask sizes. The paper also demonstrates running times on a simple morphology-based layout analysis algorithm on the UW3 database and shows that replacing bit blit morphology with run length based morphology speeds up performance approximately two-fold.

References

  1. Anderson, K. L. and Michell, J. L. (1988). System for creating transposed image data from a run end or run length encoded image. U.S. Patent #4783834.
  2. Au, K. M. and Zhu, Z. (2002). Skew processing of raster scan images. U.S. Patent #6490376.
  3. Baird, H. S., Jones, S. E., and Fortune, S. J. (1990). Image segmentation by shape-directed covers. In Proceedings of the Tenth International Conference on Pattern Recognition, Atlantic City, New Jersey, pages 820- 825.
  4. Bloomberg, D. S. (2002). Implementation efficiency of binary morphology. In International Symposium on Mathematical Morphology VI.
  5. Das, A. K. and Chanda, B. (2001). A fast algorithm for skew detection of document images using morphology. International Journal on Document Analysis and Recognition, pages 109-114.
  6. Droogenbroeck, M. V. (2002). Algorithms for openings of binary and label images with rectangular structuring elements. In Mathematical Morphology: Proceedings of the 6th International Symposium (ISMM).
  7. Droogenbroeck, M. V. and Buckley, M. (2005). Morphological erosions and openings: fast algorithms based on anchors. Journal of Mathematical Imaging and Vision, Special Issue on Mathematical Morphology after 40 Years, 22(2-3):121-142.
  8. Gil, J. and Kimmel, R. (2002). Efficient dilation, erosion, opening, and closing algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(12):1606- 1616.
  9. Gil, J. and Werman, M. (1993). Computing 2D min, median, and max filters. IEEE Trans. on Pattern Analysis and Machine Intelligence, pages 504-507.
  10. Guyon, I., Haralick, R. M., Hull, J. J., and Phillips, I. T. (1997). Data sets for OCR and document image understanding research. In Bunke, H. and Wang, P., editors, Handbook of character recognition and document image analysis, pages 779-799. World Scientific, Singapore.
  11. Keysers, D. and Breuel, T. M. (2006). Optimal line and arc detection on run-length representations. In Proceedings Graphics Recognition Workshop, LNCS. Springer.
  12. Liang, J., Piper, J., and Tang, J.-Y. (1989). Erosion and dilation of binary images by arbitrary structuring elements using interval coding. Pattern Recognition Letters, 9(3).
  13. Misra, V., Arias, J. F., and Chhabra, A. K. (1999). A memory efficient method for fast transposing run-length encoded images. In Proceedings of the Fifth International Conference on Document Analysis and Recognition (ICDAR), page 161.
  14. Najman, L. (2004). Using mathematical morphology for document skew estimation. In Proc. SPIE Document Recognition and Retrieval XI, volume 5296, pages 182-191.
  15. van den Boomgaard, R. and van Balen, R. (1992). Methods for fast morphological image transforms using bitmapped binary images. CVGIP: Graphical Models and Image Processing, 54(3):252-258.
  16. van Herk, M. (1992). A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels. Pattern Recognition Letters, 13(7):517- 521.
  17. Vincent, L. (1992). Morphological algorithms. In Mathematical Morphology in Image Processing (E. Dougherty, editor), pages 255-288. Marcel-Dekker, New York.
  18. Wong, K. Y., Casey, R. G., and Wahl, F. M. (1982). Document analysis system. IBM Journal of Research and Development, 26(6):647-656.
  19. Ye, X., Cheriet, M., and Suen, C. Y. (2001). A generic method of cleaning and enhancing data from business forms. International Journal on Document Analysis and Recognition, pages 84-96.
  20. Zhu, J., Moed, M. C., and Gorian, I. S. (1995). Method and system for fast rotation of run-length encoded images. U.S. Patent #5581635.
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Paper Citation


in Harvard Style

Breuel T. (2008). BINARY MORPHOLOGY AND RELATED OPERATIONS ON RUN-LENGTH REPRESENTATIONS . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 159-166. DOI: 10.5220/0001081501590166


in Bibtex Style

@conference{visapp08,
author={Thomas M. Breuel},
title={BINARY MORPHOLOGY AND RELATED OPERATIONS ON RUN-LENGTH REPRESENTATIONS},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001081501590166},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - BINARY MORPHOLOGY AND RELATED OPERATIONS ON RUN-LENGTH REPRESENTATIONS
SN - 978-989-8111-21-0
AU - Breuel T.
PY - 2008
SP - 159
EP - 166
DO - 10.5220/0001081501590166