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Image Segmentation and Pattern Recognition: A Novel Concept, the Histogram of Connected Elements

  • Chapter
Pattern Recognition and String Matching

Part of the book series: Combinatorial Optimization ((COOP,volume 13))

Abstract

The segmentation process is a crucial step in any computer-based vision system or application, due to its inherent difficulty and the importance of its results, which are decisive for the global efficiency of the vision system. The objective of segmentation is to individualize any different regions present in any particular image. Our main concern in this chapter is to model the image segmentation process as a pattern recognition problem, which, as an important practical corollary, implies that any method or technique from the pattern recognition field can, in principle, be applied to solve the segmentation problem in any computer-based vision system or application.

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References

  1. A.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22 No. 1 (2000) pp. 4–38.

    Article  Google Scholar 

  2. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Second Edition (John Wiley and Sons, Inc., New York, 2001 ).

    MATH  Google Scholar 

  3. D. Chen, X. Cheng, A Simple Implementation of the Stochastic Discrimination for Pattern Recognition, in F.J. Ferri, J.M. Inesta, A. Amin, P. Pudil (eds.) Advances in Pattern Recognition, LNCS 1876 (Springer, Berlin, 2000 ) pp. 882–887.

    Chapter  Google Scholar 

  4. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, ( Springer, New York, 2001 ).

    MATH  Google Scholar 

  5. K. Fukunaga, Statistical Pattern Recognition, in C.H. Chen, L.F. Pau and P.S.P. Wang (eds.) Handbook of Pattern Recognition and Computer Vision, ( Singapore, World Scientific, 1993 ) pp. 33–60.

    Chapter  Google Scholar 

  6. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, Vol I ( Addison-Wesley, Reading, Mass., 1992 ).

    Google Scholar 

  7. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, Second Edition (PWS Publishing, Pacific Grove, Ca., 1999 ).

    Google Scholar 

  8. C.A. Glasbey, G.W. Horgan, Image Analysis for the Biological Sciences, ( John Wiley and Sons, New York, 1995 ).

    MATH  Google Scholar 

  9. J. Besag, On the Statistical Analysis of Dirty Pictures (with discussions), Journal of the Royal Statistical Society, Series B 48 (1986) pp. 259–302.

    MathSciNet  MATH  Google Scholar 

  10. J. Besag, Towards Bayesian Image Analysis, Journal of Applied Statistics, Vol. 16 No. 3 (1989) pp. 295–406.

    Article  Google Scholar 

  11. A. Rosenfeld, R.A. Hummel, S.W. Zucker, Scene Labelling by Relaxation Operations, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 6 No. 6 (1976) pp. 420–433.

    Article  MathSciNet  MATH  Google Scholar 

  12. A. Rosenfeld, C. Kak, Digital Picture Processing, Second Edition, Vol. 2 (Academic Press, 1982 ).

    Google Scholar 

  13. J.A. Richards, X. Jia, Remote Sensing Digital Image Analysis, Third Edition (Springer, 1999 ).

    Google Scholar 

  14. S. Geman, D. Geman, Stochastic Relaxation, Gibbs distributions and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6No. 6(1984) pp. 722–741.

    Article  Google Scholar 

  15. R. Chelappa, A. Jain, Markov Random Fields. Theory and Applications, (Academic Press, 1993 ).

    Google Scholar 

  16. S.Z. Li, Markov Random Fields Modeling in Image Analysis, (Springer, 2001 ).

    Google Scholar 

  17. G.L. Gimelfarb, Image Textures and Gibbs Random Fields, ( Kluwer Academic Publishers, Dordrecht, 1999 ).

    Book  Google Scholar 

  18. P.K. Sahoo, S. Soltani, A.K.C. Wong, SURVEY: A Survey of Thresholding Techniques, Computer Vision, Graphics, and Image Processing, Vol. 41 (1988) pp. 233–260.

    Article  Google Scholar 

  19. J.R. Parker, Algorithms for Images Processing and Computer Vision, (John Wiley and Sons, 1997 ).

    Google Scholar 

  20. M. Seul, L. O’Gorman, M.J. Sammon, Practical Algorithms for Image Analysis, (Cambridge University Press, 2000 ).

    MATH  Google Scholar 

  21. B. Batchelor, F. Waltz, Intelligent Machine Vision: Techniques, Implementations and Applications, (Springer, 2001 ).

    Book  MATH  Google Scholar 

  22. S.J. Sangwine, R.E.N. Horne (eds.), The Colour Image Processing Handbook, ( Kluwer Academic Publishers, Boston, 1998 ).

    Google Scholar 

  23. K. Abend, T.J. Harley, L.N. Canal, Classification of Binary Random Patterns Images, IEEE Transactions on Information Theory, Vol. 11 (1965) pp. 538–544.

    Article  MATH  Google Scholar 

  24. Ch. Therrien, An Estimation-Theoretic Approach to Terrain Image Segmentation, Computer Vision, Graphics, and Image Processing, Vol. 23 (1983) pp. 313–326.

    Article  Google Scholar 

  25. Ch. Therrien, Decision, Estimation and Classification, ( John Wiley and Sons, New York, 1989 ).

    MATH  Google Scholar 

  26. R.W. Conners, C.A. Harlow, A Theoretical Comparison of Texture Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2 No. 3 (1980) pp. 204–222.

    Article  MATH  Google Scholar 

  27. M.D. Levine, Vision in Man and Machine, ( Mc Graw-Hill, New York, 1985 ).

    Google Scholar 

  28. M.A. Patricio, D. Maravall, Segmentation of Text and Graphics/Images using the Gray-Level Histogram Fourier Transform, in F.J. Ferri, J.M. Iesta, A. Amin, P. Pudil (eds.) Advances in Pattern Recognition, LNCS 1876 (Springer, Berlin, 2000 ) pp. 757–766.

    Chapter  Google Scholar 

  29. W.K. Pratt, Digital Image Processing, Third Edition ( John Wiley and Sons, New York, 2001 ).

    Book  Google Scholar 

  30. C.C. Gotlieb, H.E. Kreyszig, Texture Descriptors Based on Coocurrence Matrices, Computer Vision, Graphics, and Image Processing, Vol. 51 No. 1 (1990) pp. 70–86.

    Article  Google Scholar 

  31. M. Tuceryan, A.J. Kain, Texture Analysis, in C.H. Chen, L.F. Pau and P.S.P. Wang (eds.) Handbook of Pattern Recognition and Computer Vision, ( Singapore, World Scientific, 1993 ) pp. 235–276.

    Chapter  Google Scholar 

  32. R. Chelappa, R.L. Kashyap, B.S. Manjunath, Model-Based Texture Segmentation and Classification, in C.H. Chen, L.F. Pau and P.S.P. Wang (eds.) Handbook of Pattern Recognition and Computer Vision, ( Singapore, World Scientific, 1993 ) pp. 277–310.

    Chapter  Google Scholar 

  33. B. Julesz, Textons, the Elements of Texture Perception and their Interations, Nature, Vol.290, March 12 (1981) pp. 84–92.

    Article  Google Scholar 

  34. J.S. Weszka, C.K. Dyer, A. Rosenfeld, A Comparative Study of Texture Measure for Terrain Classification, IEEE Transactions on Systems, Man, and CyberneticsVol. 6 No. 4 (1976) pp. 269–285.

    MATH  Google Scholar 

  35. M.A. Patricio and D. Maravall, A Novel Concept: the Connected Elements Histogram and its Application to Document Binarization, In Proc. of the IX Spanish Symposium on Pattern Recognition and Image Analysis, Vol. I (2001) pp. 43–48.

    Google Scholar 

  36. M.A. Patricio and D. Maravall, Wood Texture Analysis by Combining the Connected Elements Histogram and Artificial Neural Networks, in J. Mira, A. Prieto (eds) Bio-inspired Applications of Connectionism, LNCS 2085, (Springer, Berlin, 2001 ) pp. 160–167.

    Chapter  Google Scholar 

  37. J. Serra, P. Soille, Mathematical Morphology and its Application to Image Processing, ( Kluwer Academic Publishers, Dordrecht, 1994 ).

    Book  Google Scholar 

  38. P.H. Eichel, E.J. Delp, K. Koral, A.J. Buda, Method for a Fully Automatic Definition of Coronary Arterial Edges from Cineangiograms, IEEE Transactions on Medical Imaging, Vol. 7 No. 4 (1988) pp. 313–320.

    Article  Google Scholar 

  39. R. Nekovei, T. Sun, Back-Propagation Network and its Configuration for Blood Vessel Detection in Angiograms, IEEE Transactions on Neural Networks, Vol. 6 No. 1 (1995) pp. 64–72.

    Article  Google Scholar 

  40. R. Poli, G. Balli, An Algorithm for Real-Time Vessel Enhancement and Detection, Computer Methods and Programs in Biomedicine, Vol. 52 (1997) pp. 1–22.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Spain

    Darío Maravall

  2. Departamento de Informática, Universidad Carlos III de Madrid, Spain

    Miguel Ángel Patricio

Authors
  1. Darío Maravall
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  2. Miguel Ángel Patricio
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Editor information

Editors and Affiliations

  1. University of Wisconsin — Green Bay, Green Bay, WI, USA

    Dechang Chen

  2. The George Washington University, Washington DC, USA

    Xiuzhen Cheng

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© 2003 Kluwer Academic Publishers

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Maravall, D., Patricio, M.Á. (2003). Image Segmentation and Pattern Recognition: A Novel Concept, the Histogram of Connected Elements. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_17

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  • DOI: https://doi.org/10.1007/978-1-4613-0231-5_17

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7952-2

  • Online ISBN: 978-1-4613-0231-5

  • eBook Packages: Springer Book Archive

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