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A Really Useful Vectorization Algorithm

  • Dave Elliman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

A novel algorithm for the vectorization of binary images is described. It is based on a data structure formed by crack following the outlines of the dark region of the image and applying a heuristic to decide which edges lie along the direction of the vector at any position. The regions are divided into classes, described as strokes and junctions respectively. The idea of a junction as a region containing more than one stroke is introduced, and this is used to inform the process of vectorization in these areas. The quality of the vectors produced compares favourably with those produced by other algorithms known to the author, and the implementation is reasonably efficient, a typical A4 drawing is processed in under ten seconds on my 300 MHz Pentium lap-top.

Keywords

Binary Image Machine Intelligence Dark Region Medial Axis Engineering Drawing 
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.

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References

  1. 1.
    Dori, D., Liu, W.: Sparse Pixel Vectorization, An Algorithm and its Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21 No 3 (1999) 202–215 19, 20CrossRefGoogle Scholar
  2. 2.
    Dori, D.: Orthogonal Zig-Zag: An Algorithm for Vectorizing Engineering Drawings Compared with Hough Transform. Advances in Software Engineering, Vol. 28 No 1 (1997) 11–24 20CrossRefGoogle Scholar
  3. 3.
    Elliman, D. G.: Document Recognition for Facsimile Data. Document Image Processing and Multimedia (DIPM’99), IEE Press London, March (1999) 20, 22Google Scholar
  4. 4.
    Booch, G., Jacobson, I., Rumbaugh, J.: The Unified Modelling Language Users Guide. Addison-Wesley Press, (1998) 24Google Scholar
  5. 5.
    Joseph, S. H., Pridmore, T. P.: Knowledge Directed Interpretation of Engineering Drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14 No 9 (1992) 202–215 20CrossRefGoogle Scholar
  6. 6.
    Kasturi, R., Bow, S. T., El-Masri, W., Shah, J., Gattiker, J. R., Mokate, U. B.: A System for Interpretation of Line Drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12 No 10 (1990) 978–992CrossRefGoogle Scholar
  7. 7.
    Lam, L., Lee, S., Suen, C. Y.: Thinning Methodologies-A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14 No 9 (1992) 869–885 20CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Dave Elliman
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamUK

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