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The Problem of Pattern Recognition in Arrays of Interconnected Objects. Statement of the Recognition Problem and Basic Assumptions

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Abstract

In the classical pattern recognition problem, consideration is given to individual objects, each of which actually belongs to one of the finite number of classes and is presented for the recognition irrespective of other objects. Recognition objects often form a single interconnected array determined by the nature of the event involved, namely, its natural extent in time or in space along one or a few coordinates. As a consequence, the need arises to take consistent decisions about the classes for all elements of the array. The prior assumption consisting in the fact that neighboring objects more often belong to one class than to different classes will permit one to improve the recognition quality in comparison with the classical case of the independence of classes of separate objects.

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REFERENCES

  1. Arkad'ev, A.G. and Braverman, E.M., Obuchenie mashiny raspoznavaniyu obrazov (Machine Learning of Pattern Recognition), Moscow: Nauka, 1964.

    Google Scholar 

  2. Bongard, M.M., Problema uznavaniya (A Recognition Problem), Moscow: Nauka, 1967.

    Google Scholar 

  3. Aizerman, M.A., Braverman, E.M., and Rozonoer, L.I., Metod potentsial'nykh funktsii v teorii obucheniya mashin (A Method of Potential Functions in the Theory of Machine Learning), Moscow: Nauka, 1970.

    Google Scholar 

  4. Arkad'ev, A.G. and Braverman, E.M., Obuchenie mashiny klassifikatsii ob"ektov (Machine Learning of Object Classification), Moscow: Nauka, 1971.

    Google Scholar 

  5. Zagoruiko, N.G., Metody raspoznavaniya i ikh primenenie (Recognition Methods and their Application), Moscow: Sovetskoe Radio, 1972.

    Google Scholar 

  6. Vapnik, V.N. and Chervonenkis, A.Ya., Teoriya raspoznovaniya obrazov (statisticheskie problemy obucheniya) (The Theory of Pattern Recognition (Statistical Problems of Learning)), Moscow: Nauka, 1974.

    Google Scholar 

  7. Fomin, V.N., Matematicheskaya teoriya obuchaemykh opoznayushchikh sistem (The Mathematical Theory of Learnable Recognizing Systems), Leningrad: Leningrad. Gos. Univ., 1976.

    Google Scholar 

  8. Duda, R.O. and Hart, P.E., Pattern Classification and Scene Analysis, New York: Wiley, 1973. Translated under the title Raspoznavanie obrazov i analiz stsen, Moscow: Mir, 1976.

    Google Scholar 

  9. Zhuravlev, Yu.I., On the Algebraic Approach to the Solution of Problems of Recognition or Classification, Probl. Kibern., 1978, issue 33, pp. 5-68.

  10. Tou, J.T. and Gonzalez, R.C., Pattern Recognition Principles, Reading: Addison-Wesley, 1974. Translated under the title Printsipy raspoznavaniya obrazov, Moscow: Mir, 1978.

    Google Scholar 

  11. Fukunaga, K., Introduction to Statistical Pattern Recognition, New York: Academic, 1972. Translated under the title Vvedenie v statisticheskuyu teoriyu raspoznavaniya obrazov, Moscow: Nauka, 1979.

    Google Scholar 

  12. Braverman, E.M. and Muchnik, I.B., Strukturnye metody obrabotki dannykh (Structural Methods of Data Processing), Moscow: Nauka, 1983.

    Google Scholar 

  13. Vapnik, V.N., The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.

    Google Scholar 

  14. Vapnik, V.N., Statistical Learning Theory, New York: Wiley, 1998.

    Google Scholar 

  15. Zhuravlev, Yu.I., Izbrannye nauchnye trudy (Selected Scientific Works), Moscow: Magistr, 1998.

    Google Scholar 

  16. Zagoruiko, N.G., Prikladnye metody analiza dannykh i znanii (Applied Methods of Analysis of Data and Knowledge), Novosibirsk: Novosibirsk. Inst. Mat., 1999.

    Google Scholar 

  17. Rosenfeld, A., Image Modelling, New York: Academic, 1981. Translated under the title Raspoznavanie i obrabotka izobrazhenii, Moscow: Mir, 1972.

    Google Scholar 

  18. Pratt, W.K., Digital Image Processing, New York: Wiley, 1978. Translated under the title Tsifrovaya obraborka izobrazhenii, Moscow: Mir, 1982.

    Google Scholar 

  19. Zhdanov, M.G. and Shraibman, V.I., Korrelyatsionnyi metod razdeleniya geofizicheskikh anomalii (A Correlation Method of Separation of Geophysical Anomalies), Moscow: Nedra, 1973.

    Google Scholar 

  20. Kligene, N. and Telksnis, L., Methods of Detection of Instants of Changes in the Properties of Random Processes, Avtom. Telemekh., 1983, no. 10, pp. 5-56.

  21. Vintsyuk, T.K., Analiz, raspoznovanie i interpretatsiya rechevykh signalov (Analysis, Recognition, and Interpretation of Voice Signals), Kiev: Naukova Dumka, 1987.

    Google Scholar 

  22. Rabiner, L.R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, 1989, vol. 77, no. 2, pp. 257-285.

    Google Scholar 

  23. Nemirko, A.P., Tsifrovaya obrabotka biologicheskikh signalov (Digital Processing of Biological Signals), Moscow: Nauka, 1984.

    Google Scholar 

  24. Detection of Abrupt Changes in Signals and Dynamical Systems, Basseville, M. and Benveniste, A., Eds., New York: Springer-Verlag, 1986. Translated under the title Obnaruzhenie izmeneniya svoistv signalov i dinamicheskikh sistem, Moscow: Mir, 1989.

    Google Scholar 

  25. Mottl, V.V. and Muchnik, I.B., Skrytye markovskie modeli v strukturnom analize signalov (Hidden Markov Models in Structural Analysis of Signals), Moscow: Fizmatlit, 1999.

    Google Scholar 

  26. Muchnik, I.B., Mottl, V.V., and Levyant, V.B., Massive Data Set Analysis in Seismic Explorations for Oil and Gas in Crystalline Basement Interval, Tech. Rep. 99-3, DIMACS, Rutgers Univ., USA, 1999.

  27. Duran, B.S. and Odell, P.L., Cluster Analysis: A Survey, Berlin: Springer-Verlag, 1974. Translated under the title Klasternyi analiz, Moscow: Statistika, 1977.

    Google Scholar 

  28. Mandel, I.D., Klasternyi analiz (Cluster Analysis), Moscow: Financy i Statistika.

  29. Prikladnaya statistika: Klassifikatsiya i snizhenie razmernosti (Applied Statistics: Classification and Decrease of Dimensionality), Aivazyan, S.A., Ed., Moscow: Finansy i Statistika, 1989.

    Google Scholar 

  30. Kemeny, J.G. and Snell, J.L., Finite Markov Chains, Princeton, Van Nostrand, 1960. Translated under the title Konechnye tsepi Markova, Moscow: Nauka, 1970.

    Google Scholar 

  31. Lebedev, D.A., Bezruk, A.A., and Novikov, V.M., The Markov Probability Model of an Image and a Picture, Preprint of Inst. for Information Transmission Problems, Russ. Acad. Sci., Moscow, 1983.

  32. Kodirovanie i obrabotka izobrazhenii (Coding and Processing of Images), Zyablov, V.V. and Lebedev, D.S., Eds., Moscow: Nauka, 1988.

    Google Scholar 

  33. Kickpatrick, S., Gelatt, C., and Vecci, M., Optimization by Simulated Annealing, Sci., 1983, vol. 220, pp. 671-680.

    Google Scholar 

  34. Geman, S. and Geman, D., Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Trans. PAMI, 1984, vol. 6, pp. 721-741.

    Google Scholar 

  35. Besag, J.E., On the Statistical Analysis of Dirty Pictures (with Discussions), J. Royal Stat. Soc., 1986, vol. B48, pp. 259-302.

    Google Scholar 

  36. Modestino, J.W. and Zhang, J., A Markov Random Field Model-Based Approach to Image Interpretation, IEEE Trans. PAMI, 1992, vol. 14, no. 6, pp. 606-615.

    Google Scholar 

  37. Smith, A.F.M. and Roberts, G.O., Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte-Carlo Methods, J. Royal Stat. Soc., 1993, B55, no. 1, pp. 3-23.

    Google Scholar 

  38. Tierney, L., Markov Chains for Exploring Posterior Distributions, Ann. Stat., 1994, vol. 22, no. 4, pp. 1701-1762.

    Google Scholar 

  39. Wu, C.-H. and Doerschuk, P.C., Tree Approximation to Markov Random Fields, IEEE. Trans. PAMI, 1995, vol. 17, no. 4, pp. 391-402.

    Google Scholar 

  40. Mottl, V.V., Muchnik, I.B., Blinov, A.B., and Kopylov, A.V., Hidden Tree-Like Quasi-Markov Model and Generalized Technique for a Class of Image Processing Problems, Proc. 13th ICPR'96, Austria, Vienna, 1966, vol. 2, Track B, pp. 715-719.

    Google Scholar 

  41. Muchnik, I.B. and Mottl, V.V., Bellman Functions on Trees for Segmentation, Generalized Smoothing, Matching and Multi-Alignment in Massive Data Sets, Tech. Rep. 98-15, DIMACS, Rutgers Univ., USA, Feb. 1988.

  42. Mottl, V.V., Recognition of the Flow of Random Events, Avtom. Telemekh., 1985, no. 4, pp. 92-100.

  43. Mottl, V.V. and Muchnik, I.B., A Recognition Algorithm of the Flow of Random Events, Avtom. Telemekh., 1986, no. 2, pp. 142-146.

  44. Mottl, V.V., Parametric Learning of Recognition of the Flow of Events. I, Avtom. Telemekh., 1989, no. 6, pp. 107-112.

  45. Mottl, V.V., Parametric Learning of Recognition of the Flow of Events. II, Avtom. Telemekh., 1989, no. 7, pp. 157-167.

  46. Mottl, V.V. and Muchnik, I,B., Deterministic Models and Methods of Pattern Recognition on the Time Axis. I, Avtom. Telemekh., 1991, no. 3, pp. 120-132.

  47. Mottl, V.V. and Muchnik, I.B., Deterministic Models and Methods of Pattern Recognition on the Time Axis. II, Avtom. Telemekh., 1991, no. 4, pp. 144-146.

  48. Mottl, V.V. and Muchnik, I.B., Deterministic Models and Methods of Pattern Recognition on the Time Axis. III, Avtom. Telemekh., 1991, no. 5, pp. 154-162.

  49. Dvoenko, S.D., Recognition of the Sequence of Events on the Finite Time Interval, Avtom. Telemekh., 1991, no. 5, pp. 143-153.

  50. Dvoenko, S.D., Learning of Recognition of Events on the Time Axis with Minimization of Recognition Roughness, Avtom. Telemekh., 1995, no. 8, pp. 142-150.

  51. Dvoenko, S.D., Recognition of the Sequence of Events in the Mode of Real Time, Avtom. Telemekh., 1996, no. 1, pp. 149-157.

  52. Mottle, V.V., Dvoenko, S.D., Levyant, V.B., and Muchnik, I.B., Pattern Recognition in Spatial Data: A New Method of Seismic Explorations for Oil and Gas in Crystalline Basement Rocks, Proc., 15th ICPR' 2000, Spain, Barcelona, 2000, vol. 3, pp. 210-213.

    Google Scholar 

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Dvoenko, S.D., Kopylov, A.V. & Mottl, V.V. The Problem of Pattern Recognition in Arrays of Interconnected Objects. Statement of the Recognition Problem and Basic Assumptions. Automation and Remote Control 65, 127–141 (2004). https://doi.org/10.1023/B:AURC.0000011696.31008.5a

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