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Visual Form Representation

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Human and Machine Vision
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Abstract

Schemes to compact spatial data into representations that facilitate the computation of geometrical properties and favor shape description are discussed in this paper. Any region, resulting after image segmentation, can be basically represented in terms of its external characteristics (the contour of the region), or of its internal characteristics (the pixels constituting the region). Hierarchical data structures, based on the principle of recursive decomposition, as well as approximate representations are also discussed.

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References

  1. A. Rosenfeld and A.C. Kak, Digital Picture Processing, Academic Press, New York, NY (1982).

    Google Scholar 

  2. R.C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley, Reading, MA (1987).

    Google Scholar 

  3. B.G. Batchelor, D.A. Hill, and D.C. Hodgson eds., Automated Visual Inspection, IFS North-Holland, Bedford, NL (1985).

    Google Scholar 

  4. H. Samet, The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading, MA (1989).

    Google Scholar 

  5. G. Borgefors, Distance transformations in arbitrary dimensions, Comput. Vision Graphics Image Process., Vol.27, pp. 321–345 (1984).

    Article  Google Scholar 

  6. G. Borgefors, Distance transformations in digital images, Comput. Vision Graphics Image Process., Vol.34, pp. 344–371 (1986).

    Article  Google Scholar 

  7. H. Freeman, Computer processing of line drawings, Comput. Surveys, Vol.6, pp. 57–97 (1974).

    Article  Google Scholar 

  8. T. Pavlidis, Structural pattern recognition, Springer Verlag, New York, NY (1977).

    Google Scholar 

  9. G. Gallus and P. W. Neurath, Improved computer chromosome analysis incorporating preprocessing and boundary analysis, Phys. Med. Biol, Vol.15, p. 435 (1970).

    Article  PubMed  CAS  Google Scholar 

  10. H. Freeman, On the encoding of arbitrary geometric configurations, IRE Trans. Electronic Computers, Vol.10, pp. 260–268 (1961).

    Article  Google Scholar 

  11. C.H. Teh and R.T. Chin, On the detection of dominant points on digital curves, IEEE Trans. Patt. Anal. Mach. Intell., Vol.11, p. 859 (1989).

    Article  Google Scholar 

  12. C. Arcelli and G. Ramella, Finding contour-based abstractions of planar patterns, Pattern Recognition, Vol.26, No.10, pp. 1563–1577 (1993).

    Article  Google Scholar 

  13. I. Ragnemalm and G. Borgefors, Towards a minimal shape representation using maximal disks, in The Euclidean Distance Transform, I. Ragnemalm, Dissertation No.304, Linköping University, S, p. 245 (1993).

    Google Scholar 

  14. A. Rosenfeld and J.L. Pfaltz, Distance functions on digital pictures, Pattern Recognition, Vol.1, No.1, pp. 33–61 (1968).

    Article  Google Scholar 

  15. C. Arcelli and G. Sanniti di Baja, Weighted distance transforms: a caracterization, in Image Analysis and Processing II, V. Cantoni et al. eds., Plenum Press, New York, NY, pp. 205–211 (1988).

    Chapter  Google Scholar 

  16. G. Borgefors, I. Ragnemalm, and G. Sanniti di Baja, The Euclidean distance transform: finding the local maxima and reconstructing, Proc. 7th Scandinavian Conf on Image Analysis, pp. 974-981 (1991).

    Google Scholar 

  17. G. Borgefors, Centres of maximal discs in the 5-7-11 distance transform, Proc. 8th Scandinavian Conf on Image Analysis, pp. 105-111 (1993).

    Google Scholar 

  18. C.J. Hilditch, Comparison of thinning algorithms on a parallel processor, Image Vision Comput., Vol. 1, p. 115 (1983).

    Article  Google Scholar 

  19. N.J. Naccache and R. Shinghal, An investigation into the skeletonization approach of Hilditch, Pattern Recogn., Vol.17, p. 279 (1984).

    Article  Google Scholar 

  20. F. Leymarie and M.D. Levine, Simulating the grassfire transform using an active contour model, IEEE Trans. Patt. Anal. Mach. Intell., Vol.14, No.1, pp. 56–75 (1992).

    Article  Google Scholar 

  21. L. Lam, S.W. Lee, and C.Y. Suen, Thinning methodologies-A comprehensive survey, IEEE Trans. Patt. Anal. Mach. Intell., Vol.14, p. 869 (1992).

    Article  Google Scholar 

  22. C. Arcelli, L.P. Cordella, and S. Levialdi, From local maxima to connected skeletons, IEEE Trans, on PAMI, Vol.3, pp. 134–143 (1981).

    Article  CAS  Google Scholar 

  23. C. Arcelli and G. Sanniti di Baja, A width-independent fast thinning algorithm, IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol.7, pp. 463–474 (1985).

    Article  CAS  Google Scholar 

  24. C. Arcelli and G. Sanniti di Baja, A one-pass two-operations process to detect the skeletal pixels on the 4-distance transform, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.11, pp. 411–414 (1989).

    Article  Google Scholar 

  25. L. Dorst, Pseudo-Euclidean skeletons, Proc. 8th Int. Conf. on Pattern Recognition, pp. 286-288 (1986).

    Google Scholar 

  26. C. Arcelli and M. Frucci, Reversible skeletonization by (5, 7, 11)-erosion, in Visual Form Analysis and Recognition, C. Arcelli et al. eds., Plenum, New York, NY, pp. 21–28 (1992).

    Google Scholar 

  27. C. Arcelli and G. Sanniti di Baja, Ridge Points in Euclidean Distance Maps, Pattern Recognition Letters, Vol.13, p. 237 (1992).

    Article  Google Scholar 

  28. G. Sanniti di Baja, Well-Shaped, stable and reversible skeletons from the (3, 4)-distance transform, Journal of Visual Communication and Image Representation (1993).

    Google Scholar 

  29. C. Arcelli and G. Sanniti di Baja, Euclidean skeleton via center-of-maximal-disc extraction, Image and Vision Computing, Vol.11, p. 163 (1993).

    Article  Google Scholar 

  30. L.P. Cordella and G. Sanniti di Baja, Geometric properties of the union of maximal neighbourhoods, IEEE Trans. Patt. Anal. Mach. Intell., Vol.11, pp. 214–217 (1989).

    Article  Google Scholar 

  31. G. Sanniti di Baja, O (N) computation of projections and moments from the labelled skeleton, Comput. Vision Graphics Image Process., Vol.49, p. 369 (1990).

    Article  Google Scholar 

  32. C. Arcelli, R. Colucci, and G. Sanniti di Baja, On the description of digital strips, Proc. Int. Conf. on Artificial Intelligence Applications and Neural Networks, pp. 193-196 (1990).

    Google Scholar 

  33. G. Sanniti di Baja and E. Thiel, Shape Description via Weighted Skeleton Partition, Proc. 7th Int. Conf. on Image Anal. and Process., Bari, I (in press).

    Google Scholar 

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© 1994 Springer Science+Business Media New York

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Sanniti di Baja, G. (1994). Visual Form Representation. In: Cantoni, V. (eds) Human and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1004-2_8

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  • DOI: https://doi.org/10.1007/978-1-4899-1004-2_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-1006-6

  • Online ISBN: 978-1-4899-1004-2

  • eBook Packages: Springer Book Archive

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