A Computer Vision Model for The Analysis of Classes of Hand-Written Plane Curves

  • S. Impedovo
  • M. Castellano


The objective of this paper is to research the properties that allow the characterization of classes of hand-written plane curves. To this purpose the change in shape among plane curves which belong to the same class is considered as a dynamic phenomenon where shape deformation is assumed as arising from a motion consistent with the perception of the human visual system. A preliminary investigation to select the physical laws these deformations are linked to is developed by using the velocity fields, obtained through the well known computer vision motion-techniques.


Coherence Nogo Wallach 


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  1. [1]
    S. Impedovo et al.; “A Fourier Descriptor Set for Non-stylized Numerals.” IEEE Transaction on System Man and Cybernetics, Vol. SMC-8, Aug. 1978, pp. 640–645.Google Scholar
  2. [2]
    S. Impedovo; “Power Pattern Resolution in Human Vision”, Proc. SPIE 85, Application of Artificial Intelligence II,Vol. 548, pp. 263–268.Google Scholar
  3. [3]
    L.R. Rabiner,B. Gold; “Theory and Application of Digital Signal Processing.”,Prentice Hall Inc. Englewood Cliffs, N.J. 1975.Google Scholar
  4. [4]
    V. Cappellini, A.G. Constantinides, P. Emiliani; “Digital Filters and their Applications.” Academic Press 1978 London.Google Scholar
  5. [5]
    A.V. Oppenheim and R.W. Schafer; “Digital Signal Processing”, Prentice-Hall Inc.,Englewood Cliffs, N.J. 1975.MATHGoogle Scholar
  6. [6]
    A.E. Taylor; “Introduction to Functional Analysis”, John-Wiley and Sons inc., London.Google Scholar
  7. [7]
    L. Schwartz; “Analyse-Deuxième Partie. Topologie Generale et Analyse Fonctionnelle” Hermann 1970 Paris.Google Scholar
  8. [8]
    G. Granlund; “ Fourier Preprocessing for Hand-Printed Character Recognition. ”, IEEE Trans. Comp., Vol. C.21–3, pp. 195–201, Feb. 1972.Google Scholar
  9. [9]
    E. Person and K.S. Fu; “Shape Discrimination Using Fourier Descriptors.”, IEEE Trans. Syst., Man, Cybern.,Vol. SMC-7,n. 3 pp. 170–179, Mar. 1977.CrossRefGoogle Scholar
  10. [10]
    S. Impedovo et al.; “Interactive System for Hand-Written Numerals Classification Based on Fourier Descriptors”. Proc. of the International Conference on “Image Analysis and Processing ”. PAVIA 22–24 Oct. 1980 pp. 135–139.Google Scholar
  11. [11]
    S.K. Parui, D. Dutta Majumder; “A New Definition of Shape Similarity.” Pattern Recognition Letters, Vol. 1, n. 1, pp. 37–42 1982.MATHCrossRefGoogle Scholar
  12. [12]
    S.K. Parui, D. Dutta Majumder; “Some Similarity Measures for Open Curves.”Pattern Recognition Letters,Vol.1,n. 3, pp. 129–134 1983.MATHCrossRefGoogle Scholar
  13. [13]
    J. Sklansky; “Pattern Recognition.Introduction and Foundations” Dowden, Hatchinson and Ross Inc. 1973, John Wiley and Sons inc.-London.Google Scholar
  14. [14]
    H. Wallach; “On Preceived Identity:1. the direction of motion of straight lines.” In On perception (ed. H.Wallach).New York: Quadrangle 1976.Google Scholar
  15. [15]
    C.I. Fennema,W.B. Thompson; “Velocity Determination in Scenes Containing Several Moving Objects.” Comp. Graph, and Image Processing 9, 1979, pp. 301–315.CrossRefGoogle Scholar
  16. [16]
    D. Marr, S. Ullman; “Directional Selectivity and Its Use in Early Visual Processing.”, Proc. R. Soc.Lond.B 211, 1981, pp. 151–180CrossRefGoogle Scholar
  17. [17]
    E.H. Adelson, & J.A. Movshon; “Phenomenal Coherence of Moving Visual Patterns.” Nature, Lond. 300, 1982, pp. 523–525.CrossRefGoogle Scholar
  18. [18]
    T. Poggio; “Visual Algorithms.” In Physical and Biological Processing of Images, (ed. O.J. Braddick & A.C. Sleigh) Berlin: Springer-Verlag, 1983.Google Scholar
  19. [19]
    A.L. Yuille; “The Smoothest Velocity Field and Taken Matching Schemes.”, M.I.T. Artif.Intell.Lab.Memo 724, 1983.Google Scholar
  20. [20]
    E.C. Hildreth;“The Computation of the Velocity Field” Proc. R. Soc. Lond. B 221, 1984, pp. 189–220.CrossRefGoogle Scholar
  21. [21]
    H.H.Nagel; “Recent Advances in Image Sequence Analysis” Premier Colloque Image — Traitment, Synthèse, Technologie et Application; Biarritz-Mai 1984.Google Scholar
  22. [22]
    R.K.P. Horn and B.G. Schunck; “Determining Optical Flow”, Artificial Intelligence 17, 1981, pp. 185–203.CrossRefGoogle Scholar
  23. [23]
    J.M. Prager, M.A. Arbib; “Computing the Optical Flow: The Match Algorithm and Prediction”, Computer Vision, Graphics and Image Processing n. 24, 1983, pp. 271–304.CrossRefGoogle Scholar
  24. [24]
    S. Impedovo; “Plane Curve Classification Through Fourier Descriptors. An Application to Arabic Hand-Written Numeral Recognition”, IEEE Computer Society Press. Proc. of the 7-th Int. Conf. on PATTER RECOGNITION Vol. 2, Aug.1984, pp. 1069–1072.Google Scholar
  25. [25]
    S. Impedovo et al.; “Surface Detection Algorithm in Three- Dimensional Complex-Space.” Cybernetic System:Recognition, Learning, Self-Organization. Edit by E.R. Caianello and G. Musso.Research Studies Press LTD.J.W. and S.Inc. 1984, pp 157–168.Google Scholar
  26. [26]
    A.N. Kolmogorov and S.V. Fomin; “Elementy Teorìì Funktsij i Funktsional’ Nogo Analiza”, Copyright by Nauka, Moskov U.R.S.S. Cap. VII Sec.1.Google Scholar
  27. [27]
    W. Rudin; “Functional Analysis”,New York: McGraw-Hill,1973Google Scholar
  28. [28]
    E.C. Hildreth; “The Measurement of Visual Motion”,A.C.M. distinguished dissertation series, Cambridge, Massachusetts M.I.T. Press.Google Scholar

Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • S. Impedovo
    • 1
  • M. Castellano
    • 2
  1. 1.Istituto di Scienze dell’InformazioneUniversità degli Studi di BariBariItaly
  2. 2.Istituto Nazionale di Fisica NucleareBariItaly

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