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A pyramidal image segmentation algorithm

  • Articles from the Russian Journal Informatsionnye Protsessy
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Abstract

The problem of image segmentation (division into homogeneous regions) basing on color and texture region differences is considered. A two-level hierarchical pyramidal segmentation algorithm is proposed for solution of this problem. The homogeneity criterion is the estimated adjacency of the image elements and regions in the combined color-texture feature space. A metric in this space is introduced and studied. The results are verified on a set of test images of different types.

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References

  1. R. Gonzalez and R. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, New Jersey, 2002; Tekhnosfera, Moscow, 2005).

  2. W. Pratt, Digital Image Processing (Wiley, New York, 1978; Mir, Moscow, 1982).

    Google Scholar 

  3. A. Rosenfeld and A. C. Kak, Digital Picture Processing (Academic, New York, 1982), Vols. 1, 2.

    Google Scholar 

  4. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973; Mir, Moscow, 1976).

    MATH  Google Scholar 

  5. L. G. Roberts, “Machine Perception of Three-Dimensional Solids,” in Optical and Electro-Optical Information Processing (MIT Press, Cambridge, Mass., 1965), pp. 159–197.

    Google Scholar 

  6. I. E. Sobel, Camera Models and Machine Perception, PhD. Thesis (Stanford Univ., Palo Alto, Calif., 1970).

    Google Scholar 

  7. J. M. S. Prewitt, “Object Enhancement and Extraction.” Picture Processing and Psychopictorics (Academic, New York, 1970), pp. 75–150.

    Google Scholar 

  8. Ya. A. Furman, A. V. Krevetskii, A. K. Peredreev, et al., Introduction to Contour Analysis and Its Applications to Image and Signal Processing (Fizmatlit, Moscow, 2003) [in Russian].

    Google Scholar 

  9. J. J. Clark, “Authenticating Edges Produced by Zero-Crossing Algorithms,” IEEE Trans. Pattern. Anal. Mach. Intell. 12, 830–831 (1989).

    Google Scholar 

  10. J. Canny, “A Computational Approach for Edge Detection,” IEEE Trans. Pattern. Anal. Mach. Intell. 8, 679–698 (1986).

    Article  Google Scholar 

  11. P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chan, “A Survey of Thresholding Techniques,” Comput. Vis. Graph. Image Process. 4, 233–260 (1988).

    Article  Google Scholar 

  12. R. Jain, R. Kasturi, and B. Schunk, Computer Vision (McGraw-Hill, New York, 1995).

    Google Scholar 

  13. Image Analysis and Mathematical Morphology, Ed. by J. Serra (Academic, New York, 1988), Vol. 2.

    Google Scholar 

  14. “Special Issue on Mathematical Morphology and Nonlinear Image Processing,” Pattern Recogn., 33, 875–1117 (2000).

  15. K. S. Fu and J. K. Mui, “A Survey of Image Segmentation,” Pattern Recogn. 13, 3–16 (1981).

    Article  MathSciNet  Google Scholar 

  16. R. M. Haralick and L. G. Shapiro, “Image Segmentation Techniques,” Comput. Vis. Graph. Image Process. 29, 100–132 (1985).

    Article  Google Scholar 

  17. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision (Addison-Wesley, Reading, MA, 1993), Vol. 2.

    Google Scholar 

  18. L. G. Shapiro and G. C. Stockman, Computer Vision (Prentice Hall, Upper Saddle River, N. J., 2001).

  19. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data (Prentice Hall, Englewood Cliffs, N. J., 1988).

    MATH  Google Scholar 

  20. J. Matas and J. Kittler, Spatial and Feature Space Clustering: Applications in Image Analysis (Proc. 6th Int. Conf. on Computer Analysis and Patterns, Czech. Republic, Prague, Sept., 1995) (Springer-Verlag, Prague, 1995).

    Google Scholar 

  21. B. Jahne, Digital Image Processing: Concepts, Algorithms, and Scientific Applications (Springer-Verlag, Berlin, 1991; Tekhnosfera, Moscow, 2007).

    Google Scholar 

  22. Y. Ohta and T. Kanade, T. Sakai, “Color Information for Region Segmentation,” Comput. Graph. Image Process. 13, 224–241 (1980).

    Article  Google Scholar 

  23. N. K. Pal and S. K. Pal, “A Review on Image Segmentation Techniques,” Pattern Recogn. 26, 1277–1293 (1993).

    Article  Google Scholar 

  24. R. M. Haralick, “Image Texture Survey.” Fundamentals in Computer Vision (Cambridge Univ. Press, Cambridge, 1983), pp. 145–172.

    Google Scholar 

  25. R. M. Haralick, “Statistical and Structural Approaches to Textures,” Proc. IEEE 67, 786–804 (1979).

    Article  Google Scholar 

  26. A. C. Bovik, M. Clark, and W. S. Geisler, “Multichannel Texture Analysis Using Localized Spatial Filters,” IEEE Trans. Pattern. Anal. Mach. Intell. 12, 55–73 (1990).

    Article  Google Scholar 

  27. L. Van Gool, P. Dewaele, and A. Oosterlinck, “Texture Analysis Anno 1983,” Comput. Vis. Graph. Image Process. 29, 336–357 (1985).

    Article  Google Scholar 

  28. S. J. Roan and J. K. Aggarwal, “Multiple Resolution Imagery and Texture Analysis,” Pattern Recogn. 20, 17–31 (1987).

    Article  Google Scholar 

  29. T. Chang and C. J. Kuo, “Texture Alalysis and Classification with Tree-Structured Wavelet Transform,” IEEE Trans. Image Process. 2, 429–441 (1993).

    Article  Google Scholar 

  30. O. Pichler, A. Teuner, and B. J. Hosticka, “A Comparison of Texture Feature Extraction Using Adaptive Gabor Filtering Pyramidal and Tree Structured Wavelet Transforms,” Pattern Recogn. 29, 733–742 (1996).

    Article  Google Scholar 

  31. A. K. Jain and F. Farrokhnia, “Unsupervised Texture Segmentation Using Gabor Filters”, Pattern Recogn. 24, 1167–1186 (1991).

    Article  Google Scholar 

  32. D. Dunn and W. E. Higgins, “Optimal Gabor Filters for Texture Segmentation,” IEEE Trans. Image Process. 4, 947–964 (1995).

    Article  Google Scholar 

  33. T. P. Weldon, W. E. Higgins, and D. F. Dunn, “Efficient Gabor Filter Design for Texture Segmentation,” Pattern Recogn. 29, 2005–2015 (1996).

    Article  Google Scholar 

  34. E. J. Carton and J. S. Weszka, A. Rosenfeld, Some Basic Texture Analysis Techniques. TR-288 (Computer Vision Laboratory, Computer Science Center, Univ. of Maryland, 1974).

  35. A. K. Jain, “Color Distance and Geodesics in Color 3 Space,” J. Opt. Soc. Am. 62, 1287–1291 (1972).

    Article  Google Scholar 

  36. D. L. MacAdam, “Projective Transformations of the ICI Color Specifications,” J. Opt. Soc. Am. 27, 294–299 (1935).

    Article  Google Scholar 

  37. G. M. Hunter and K. Steiglitz, “Operation of Images Using Quad Trees,” IEEE Trans. Pattern. Anal. Mach. Intell., 1, 145–153 (1979).

    Article  Google Scholar 

  38. A. Rosenfeld, “Quadtrees and Pyramids for Pattern Recognition and Image Analysis,” in Proc. 5th Int. Conf. on Pattern Recognition, Miami Beach, Dec., 1980 (IEEE, New York, 1980), p. 802–811.

    Google Scholar 

  39. P. Brodatz, Textures: A Photographic Album for Artists and Designers (Dover, New York, 1966).

    Google Scholar 

Download references

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Original Russian Text © P.A. Chochia, 2010, published in Informatsionnye Protsessy, 2010, Vol. 10, No. 1, pp. 23–35.

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Chochia, P.A. A pyramidal image segmentation algorithm. J. Commun. Technol. Electron. 55, 1550–1560 (2010). https://doi.org/10.1134/S1064226910120296

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