Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

  • Cristina Madalina Noaica
  • Robert Badea
  • Iulia Maria Motoc
  • Claudiu Gheorghe Ghica
  • Alin Cristian Rosoiu
  • Nicolaie Popescu-Bodorin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)

Abstract

This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.

Keywords

crisp human perception fuzzy artificial perception perceptron 

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References

  1. 1.
    Ahangar, R.G., Ahangar, M.F.: Handwritten Farsi Character Recognition using Artificial Neural Network. International Journal of Computer Science and Information Security 4(1 & 2) (2009)Google Scholar
  2. 2.
    Azoff, E.M.: Neural Network Time Series Forecasting of Financial Markets. John Wiley & Sons Inc. (1994)Google Scholar
  3. 3.
    Balas, V.E., Motoc, I.M., Barbulescu, A.: Combined Haar-Hilbert and Log-Gabor Based Iris Encoders. In: Balas, V.E., Fodor, J., Varkonyi-Koczy, A. (eds.) New Concepts and Applications in Soft Computing. Studies in Computational Intelligence, vol. 417, pp. 1–26. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Bhattacharjee, D., Basu, D.K., Nasipuri, M., Kundu, M.: Human Face Recognition Using Fuzzy Multilayer Perceptron. Journal of Soft Computing - A Fusion of Foundations, Methodologies and Applications Achive 14, 559–570 (2010)Google Scholar
  5. 5.
    Coates, A., Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Ng, A.Y.: Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning. In: Proc. 11th Int. Conf. on Document Analysis and Recognition, ICDAR (2011)Google Scholar
  6. 6.
    Copeland, B.J.: What is Artificial Intelligence? In: The Turing Archive for the History of Computing, http://www.alanturing.net/ (retrieved on May 29, 2012)
  7. 7.
    Jain, L.C., Martin, N.M.: Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications. CRC Press (1998)Google Scholar
  8. 8.
    Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. MIT Press (1998)Google Scholar
  9. 9.
    Kohonen, T.: The Self-Organizing Map. Proc. of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  10. 10.
    McCullock, W., Pitts, W.: A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)CrossRefGoogle Scholar
  11. 11.
    Motoc, I.M., Noaica, C.M., Badea, R., Ghica, C.G.: Noise Influence on the Fuzzy-Linguistic Partitioning of Iris Code Space. In: 5th International Workshop on Soft Computing Applications, Szeged, Hungary, August 22-24 (2012)Google Scholar
  12. 12.
    Popescu-Bodorin, N., Balas, V.E., Motoc, I.M.: 8-Valent Fuzzy Logic for Iris Recognition and Biometry. In: Proc. 5th IEEE Int. Symp. on Computational Intelligence and Intelligent Informatics, pp. 149–154. IEEE Press (2011)Google Scholar
  13. 13.
    Popescu-Bodorin, N., Balas, V.E., Motoc, I.M.: Iris Codes Classification Using Discriminant and Witness Directions. In: Proc. 5th IEEE Int. Symp. on Computational Intelligence and Intelligent Informatics, pp. 143–148. IEEE Press (2011)Google Scholar
  14. 14.
    Popescu-Bodorin, N., Balas, V.E.: From Cognitive Binary Logic to Cognitive Intelligent Agents. In: Proc. 14th Int. Conf. on Intelligent Engineering Systems, pp. 337–340. Conference Publishing Services - IEEE Computer Society (May 2010)Google Scholar
  15. 15.
    Rosenblatt, F.: The Perceptron, A Perceiving and Recognizing Automaton. Report No. 85-460-1, Cornell Aeronautical Laboratory (January 1957)Google Scholar
  16. 16.
    Rosenblatt, F.: Pattern Recognizing Apparatus. U.S. Patent No. 3192505 (July 14, 1961/June 29, 1965)Google Scholar
  17. 17.
    Sugeno, M., Yasukawa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Trans. on Fuzzy Systems 1(1), 7–31 (1993)CrossRefGoogle Scholar
  18. 18.
    Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yamakawa, T.: Pattern recognition hardware system employing a fuzzy neuron. In: Proc. Int. Conf. on Fuzzy Logic and Neural Networks, Iizuka, Japan, pp. 943–948 (July 1990)Google Scholar
  20. 20.
    Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo fuzzy neuron and its applications to system identification and prediction of the system behavior. In: Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks, Iizuka, Japan, pp. 477–483 (1992)Google Scholar
  21. 21.
    Zadeh, L.A.: A New Direction in AI - Toward a Computational Theory of Perceptions. AI Magazine 22(1), 73–84 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristina Madalina Noaica
    • 1
  • Robert Badea
    • 1
  • Iulia Maria Motoc
    • 1
  • Claudiu Gheorghe Ghica
    • 1
    • 2
  • Alin Cristian Rosoiu
    • 3
  • Nicolaie Popescu-Bodorin
    • 1
  1. 1.Artificial Intelligence & Computational Logic Laboratory, Mathematics & Computer Science Dept.Spiru Haret UniversityBucharestRomania
  2. 2.Clintelica ABStockholmSweden
  3. 3.Game Tester at UbiSoftBucharestRomania

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