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Affinities between Perceptual Granules: Foundations and Perspectives

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Human-Centric Information Processing Through Granular Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 182))

Abstract

This chapter gives a concise overview of the foundations of a perceptual near set approach to the discovery of affinities between perceptual objects and perceptual granules that provide a basis for perceptual systems useful in science and engineering. A perceptual object is something perceptible to the senses or knowable by the mind. Perceptual objects that have the same appearance are considered to be perceptually near each other, i.e., perceived objects that have perceived affinities or, at least, similar descriptions. A perceptual granule is a set of perceptual objects originating from observations of the objects in the physical world. Near set theory provides a basis for observation, comparison and classification of perceptual granules. By considering nearness relations in the context of a perceptual system, it is possible to gauge affinities (nearness) perceptual objects. Two kinds of indiscernibility relations and a tolerance relation make it possible to define various nearness relations. Examples of near images as perceptual systems are presented. The main contribution of this chapter is the introduction of a formal basis for discovering affinities between perceptual information granules.

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References

  1. Bargiela, A., Pedrycz, W.: Granular Computing. Kluwer, Boston (2003)

    MATH  Google Scholar 

  2. Calitoiu, D., Oommen, B., Nussbaum, D.: Desynchronizing a chaotic pattern recognition neural network to model inaccurate perception. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 37(3), 692–704 (2007)

    Article  MathSciNet  Google Scholar 

  3. Darwin, C.: On the Origin of the Species by Means of Natural Selection. J. Murray, Oxford (1859)

    Google Scholar 

  4. Fahle, M., Poggio, T.: Perceptual Learning. The MIT Press, Cambridge (2002)

    Google Scholar 

  5. Henry, C., Peters, J.F.: Image pattern recognition using approximation spaces and near sets. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS, vol. 4482, pp. 475–482. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Jähne, B.: Digital Image Processing, 6th edn. Springer, Heidelberg (2005)

    Google Scholar 

  7. Merleau-Ponty, M.: Phenomenology of Perception, trans. by Colin Smith. Smith, Callimard and Routledge & Kegan Paul, Paris and New York (1945, 1956)

    Google Scholar 

  8. Murray, J., Bradley, H., Craigie, W., Onions, C.: The Oxford English Dictionary. Oxford University Press, Oxford (1933)

    Google Scholar 

  9. Nasrabadi, N., Pal, S., King, R.: Entropy-coded hybrid differential pulse-code modulation. Electronics Letters 16(2), 63–65 (1983)

    Article  Google Scholar 

  10. Orłowska, E.: Incomplete Information: Rough Set Analysis. Studies in Fuzziness and Soft Computing, vol. 13. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  11. Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29, 27–39 (1984)

    Article  MathSciNet  Google Scholar 

  12. Pal, N.R., Pal, S.K.: Entropy: A new definition and its applications. IEEE Trans. on Sys., Man and Cybernetics 21(5), 1260–1270 (1981)

    Article  Google Scholar 

  13. Pal, N.R., Pal, S.K.: Entropic thresholding. Signal Processing 16(2), 97–108 (1983)

    Article  Google Scholar 

  14. Pal, N.R., Pal, S.K.: Object-background segmentation using new definitions of entropy. IEE Proceedings-E 136(4), 284–295 (1989)

    Google Scholar 

  15. Pal, N.R., Pal, S.K.: Entropy: A new definition and its applications. IEEE Trans. Syst, Man and Cybernetics SMC-21(5), 1260–1270 (1991)

    Article  Google Scholar 

  16. Pal, N.R., Pal, S.K.: Higher order fuzzy entropy and hybrid entropy of a set. Information Sciences 61(3), 211–231 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  17. Pal, N.R., Pal, S.K.: Some properties of the exponential entropy. Information Sciences 66(3), 119–137 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  18. Pal, S.K., King, R.A., Hashim, A.A.: Automatic gray level thresholding through index of fuzziness and entropy. Pattern Recognition Letters 1, 141–146 (1983)

    Article  Google Scholar 

  19. Pavel, M.: Fundamentals of Pattern Recognition, 2nd edn. Marcel Dekker, Inc., N.Y (1993)

    MATH  Google Scholar 

  20. Pawlak, Z.: Classification of objects by means of attributes. Polish Academy of Sciences 429 (1981)

    Google Scholar 

  21. Pawlak, Z., Peters, J.F.: Jak blisko. Systemy Wspomagania Decyzji I, 57 (2007)

    Google Scholar 

  22. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177, 41–73 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  23. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177, 28–40 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  24. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  25. Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  26. Peters, J.F.: Classification of objects by means of features. In: Proc. IEEE Symposium Series on Foundations of Computational Intelligence (IEEE SCCI 2007), Honolulu, Hawaii, pp. 1–8 (2007)

    Google Scholar 

  27. Peters, J.: Near sets. general theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2629 (2007)

    MATH  MathSciNet  Google Scholar 

  28. Peters, J.F.: Near sets. special theory about nearness of objects. Fundamenta Informaticae 75(1-4), 407–433 (2007)

    MATH  MathSciNet  Google Scholar 

  29. Peters, J.F.: Near sets. Toward approximation space-based object recognition. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 22–33. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  30. Peters, J.: Discovery of perceputally near information granules. In: Novel Developments in Granular Computing: Applications of Advanced Human Reasoning and Soft Computation. Information Science Reference, Hersey, N.Y., U.S.A (to appear, 2008)

    Google Scholar 

  31. Peters, J.F., Skowron, A.: Zdzisław pawlak: Life and work. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 1–25. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  32. Peters, J.F., Skowron, A., Stepaniuk, J.: Nearness in approximation spaces. In: Proc. Concurrency, Specification and Programming (CS&P 2006), Humboldt Universität, pp. 435–445 (2006)

    Google Scholar 

  33. Peters, J.F., Skowron, A., Stepaniuk, J.: Nearness of objects: Extension of approximation space model. Fundamenta Informaticae 79(3-4), 497–512 (2007)

    MATH  MathSciNet  Google Scholar 

  34. Peters, J.F., Wasilewski, P.: Foundations of near sets. Information Sciences (submitted, 2008)

    Google Scholar 

  35. Polkowski, L.: Rough Sets. Mathematical FoundationsGermany. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  36. Ross, T.J.: Fuzzy Logic with Engineering Applications. John Wiley & Sons, New York (2004)

    MATH  Google Scholar 

  37. Zeeman, E.: The topology of the brain and visual perception. In: Fort Jr., M.K. (ed.) Topology of 3-Manifolds and Related Topics, University of Georgia Institute, pp. 240–256. Prentice-Hall, Inc., Englewood Cliffs (1961)

    Google Scholar 

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Peters, J.F., Ramanna, S. (2009). Affinities between Perceptual Granules: Foundations and Perspectives. In: Bargiela, A., Pedrycz, W. (eds) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92916-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-92916-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92915-4

  • Online ISBN: 978-3-540-92916-1

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