Typical Segment Descriptors: A New Method for Shape Description and Identification

  • Nancy Aimé Alvarez-Roca
  • José Ruiz-Shulcloper
  • Jose M. Sanchiz-Marti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


In this paper we introduce a new method for recognizing and classifying images based on concepts derived from Logical Combinatorial Pattern Recognition (LCPR). The concept of Typical Segment Descriptor (TSD) is introduced, and algorithms are presented to compute TSDs sets from several chain code representations, like the Freeman chain code, the first differences chain code, and the vertex chain code. The typical segment descriptors of a shape are invariant to changes in the starting point, translations and rotations, and can be used for partial occlusion detection. We show several results of shape description problems pointing out the reduction in the length of the description achieved.


Shape Description Chain Code Circular Index Supervise Pattern Recognition Learning Matrix 
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.
    Sanchiz, J.M., Pla, F.: Feature Correspondence and Motion Recovery in Vehicle Planar Navigation. Pattern Recognition 32, 1961–1977 (1999)CrossRefGoogle Scholar
  2. 2.
    Agam, G., Dinstein, I.: Geometric Separation of Partially Overlapping Nonrigid Objects Applied to Automatic Chromosome Classification. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 1212–1222 (1997)CrossRefGoogle Scholar
  3. 3.
    Bribiesca, E., Guzman, A.: How to describe Pure Form and How to Measure Diffrences in Shapes Using Shape Numbers. Pattern Recognition 12, 101–112 (1980)CrossRefGoogle Scholar
  4. 4.
    Pajares, G., de la Cruz, J.M.: Computer Vision, Digital Images and Applications, RA-MA, Madrid (2001)Google Scholar
  5. 5.
    Marchand-Maillet, S., Sharaiha, Y.M.: Binary Digital Image Processing: A Discrete Approach. Academic Press, London (2000)zbMATHGoogle Scholar
  6. 6.
    Hatef, M., Kittler, J.: Combining Symbolic with Numeric Attributes in Multiclass Object Recognition Problems. In: Proceedings of the Second International Conference on Image Processing, Washington D.C, vol. 3, pp. 364–367 (1995)Google Scholar
  7. 7.
    Hsu, J.C., Hwang, S.H.: A Machine Learning Approach for Acquiring Descriptive Classification Rules for Shape Contours. Pattern Recognition 30, 245–252 (1997)CrossRefGoogle Scholar
  8. 8.
    Freeman, H.: Techniques for the Digital Computer Analysis of Chain-encoded Arbitrary Plane Curves. In: Proceedings of Nat. Electr. Conf., vol. 17, pp. 421–432 (1961)Google Scholar
  9. 9.
    Freeman, H.: Computer Processing of Line-drawing Images. Computing Surveys 6, 57–97 (1974)zbMATHCrossRefGoogle Scholar
  10. 10.
    Loncaric, S.: A Survey of Shape Analysis Techniques. Pattern Recognition 31, 983–1001 (1998)CrossRefGoogle Scholar
  11. 11.
    Madhvanath, S., Kim, G., Govindaraju, V.: Chaincode Contour Processing for Handwritten Word Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 928–932 (1999)CrossRefGoogle Scholar
  12. 12.
    Bribiesca, E.: A New Chain Code. Pattern Recognition 32, 235–251 (1999)CrossRefGoogle Scholar
  13. 13.
    Bribiesca, E.: A Chain Code for Representing 3D Curves. Pattern Recognition 33, 755–765 (2000)CrossRefGoogle Scholar
  14. 14.
    Valev, V.: A Method of Solving Pattern or Image Recognition Problems by Learning Boolean Formulas. In: Proceedings of 11th International Conference on Pattern Recognition, vol. 2, pp. 359–362 (1992)Google Scholar
  15. 15.
    Valev, V., Radeva, P.: Structural Pattern recognition by Non-Reducible Descriptors. Shape, Structure and Pattern Recognition. World Sci. Publ., 221–230 (1995)Google Scholar
  16. 16.
    Djukova, E.V.: Pattern Recognition Algorithms of the Kora Type. Pattern Recognition, Classification, Forecasting – Mathematical Techniques and their Applications 2, 99–125 (1989)Google Scholar
  17. 17.
    Lazo, M., Douglas, M., Quintana, T.: Tests for classes: An application on character recognition. In: Proceedings of III Iberoamerican Workshop on Pattern Recognition, pp. 229–236 (1998)Google Scholar
  18. 18.
    Pico, R., Almagro, G., Mena, J.: Logical Combinatorial Pattern Recognition application for classification of pathological mushrooms of sugar cane. In: Proceedings of III Iberoamerican Workshop on Pattern Recognition, pp. 279–292 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nancy Aimé Alvarez-Roca
    • 1
  • José Ruiz-Shulcloper
    • 2
  • Jose M. Sanchiz-Marti
    • 3
  1. 1.Departamento de Ciencia de la ComputaciónUniversidad de OrienteSantiago de CubaCuba
  2. 2.Laboratorio de Reconocimiento de PatronesInstituto de Cibernética, Matemáticas y FísicaLa HabanaCuba
  3. 3.Departamento de Ingeniería y Ciencia de los ComputadoresUniversidad Jaume ICastellónSpain

Personalised recommendations