Visual Form pp 633-640 | Cite as

Characteristic Pattern Based on Mathematical Morphology

  • Dongming Zhao


Shape analysis is a very important issue in image analysis and computer vision. This paper describes a methodology using morphological operations to classify shapes by their decomposed components according to morphological structuring elements. A method called characteristic pattern is introduced to extract unique representations of an object shape among other shapes. Shape size analysis using mathematical morphology was introduced by Serra [1] where size criteria are discussed and geometrical properties of morphological processing on shapes are presented with morphological measurement. With size criteria, local area size parameters and global shape size distribution are both counted in Lebesque measure. Recent development of shape distribution can be found in [3,4,5,9]. In [5], pattern spectrum is used to describe the size distribution. Shape decomposition is another approach toward shape analysis, similar to a morphological skeletonization process. In [9], decomposition is completed by using a simplest object component (a disk) and analysis of an image is through a union of disks. The characteristic pattern concept is introduced in Section 2 of this paper to provide another approach toward shape analysis and matching. The basic concept is to derive a specific pattern associated with the object shape while other shapes within sample space do not possess this pattern orientation. We call this pattern orientation characteristic pattern. Characteristic pattern can be used to identify objects and classify shapes through decomposition — a parallel process using only local neighborhood pixel information.


Characteristic Pattern Sample Space Pattern Anal Shape Analysis Object Shape 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, 1982.zbMATHGoogle Scholar
  2. [2]
    G. Matheron, Random Set and Integral Geometry, Wiley, New York, 1975.Google Scholar
  3. [3]
    R. M. Haralick, S. R. Sternberg, X. Zhuang, Image Analysis using Mathematical Morphology, IEEE Trans. Pattern Anal. Machine Intell., PAMI-9, pp. 532–550, July, 1987.CrossRefGoogle Scholar
  4. [4]
    X. Zhuang and R.M. Haralick, Morphological structuring element decomposition, Comput. Vision Graph. Image Processing, 35:370–382, 1986.zbMATHCrossRefGoogle Scholar
  5. [5]
    P. Maragos, Pattern Spectrum and Multiscale Shape Recognition, IEEE Trans. Pattern Anal. Machine Intell., PAMI-11, pp. 701–716, July 1989.CrossRefGoogle Scholar
  6. [6]
    A.R. Dill, M.D. Levine, P.B. Noble, Multiple resolution skeletons, IEEE Trans. Pattern Anal. Machine Intell., PAMI-9, pp 495–504, July 1987.CrossRefGoogle Scholar
  7. [7]
    J. Serra, Ed., Image Analysis and Mathematical Morphology, Vol. 2. Academic Press, New York, 1988.Google Scholar
  8. [8]
    C. R. Giardina and E. R. Dougherty, Morphological Methods in Image and Signal Processing, Prentice, Englewood Cliffs, NJ, 1987.Google Scholar
  9. [9]
    I. Pitas and A.N. Venetsanopoulos, Morphological shape decomposition, IEEE Trans. Pattern Anal. Machine Intell., PAMI-12, pp 38–45, Jan. 1990.CrossRefGoogle Scholar
  10. [10]
    F. Leymarie and M. D. Levine, Curvature morphology, Technical Report TRCIM-88-26, McGill Research Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada.Google Scholar
  11. [11]
    J. Xu, Decomposition of convex polygonal morphological structuring elements into neighborhood subsets, IEEE Trans. Pattern Anal. Machine Intell., PAMI-13, pp 153–162, Feb. 1991.CrossRefGoogle Scholar
  12. [12]
    P. K. Ghosh, A mathematical model for shape description using minkowski operators, Comput. Vision Graph. Image Processing, 44:239–269, 1989.CrossRefGoogle Scholar
  13. [13]
    D. Sinha and C. R. Giardina, Discrete black and white object recognition via morphological functions. IEEE Trans. Pattern Anal. Machine Intell., PAMI-12, pp 275–293, March 1990.CrossRefGoogle Scholar
  14. [14]
    Z. Zhou and A. N. Venetsanopoulos, Generic ribbons: a morphological approach towards natural natural shape decomposition. In Visual Communications and Image Processing IV (1989), pp. 170-180, Phil. PA, Nov. 1989. SPIE-1199, SPIE-1199.Google Scholar
  15. [15]
    E. R. Dougherty and C. R. Giardina, Closed-form representation of convolution, dilation, and erosion in the context of image algebra. In Proc. Computer Vision and Pattern Recognition 88, pp. 754–759, Ann Arbor, MI, June 1988.Google Scholar
  16. [16]
    T. Pavlidis, A review of algorithms for shape analysis, Comput. Vision Graph. Image Processing, 7:243–258, April 1978.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Dongming Zhao
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of Michigan-DearbornDearbornUSA

Personalised recommendations