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Abstract

Even the simplest machine vision tasks cannot be solved without the help of recognition. Pattern recognition is used for region and object classification, and basic methods of pattern recognition must be understood in order to study more complex machine vision processes.

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References

  1. E H L Aarts, and P J M van Laarhoven: Simulated annealing: a pedestrian review of the theory and some applications. In P A Devijver and J Kittler, editors, Pattern Recognition Theory and Applications, pages 179–192. Springer Verlag, Berlin-New York-Tokyo, 1986.

    Google Scholar 

  2. A P H Ambler: A versatile system for computer controlled assembly. Artificial Intelligence, 6 (2): 129–156, 1975.

    Article  MathSciNet  MATH  Google Scholar 

  3. D J Amit: Modeling Brain Function: The World of Attractor Neural Networks. Cambridge University Press, Cambridge, England; New York, 1989.

    Google Scholar 

  4. D H Ballard and C M Brown. Computer Vision. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    Google Scholar 

  5. A Barrero: Inference of tree grammars using negative samples. Pattern Recognition, 24 (1): 1–8, 1991.

    Article  MathSciNet  Google Scholar 

  6. H G Barrow and R J Popplestone. Relational descriptions in picture processing. Machine Intelligence, 6, 1971.

    Google Scholar 

  7. R Beale, and T Jackson: Neural Computing — An Introduction. Adam Hilger, Bristol, 1990.

    Book  MATH  Google Scholar 

  8. Berge 76] C Berge. Graphs and Hypergraphs. American Elsevier, New York, 2nd edition, 1976.

    Google Scholar 

  9. J R Bittner, and E M Reingold: Backtrack programming techniques. Communications of the ACM, 18 (11): 651–656, 1975.

    Article  Google Scholar 

  10. A Blum, and R L Rivest: Training a three node neural network is np-complete. In Proceedings of IEEE Conference on Neural Information Processing Systems, page 494, 1988.

    Google Scholar 

  11. C Bouman, and B Liu: Multiple resolution segmentation of textured images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (2): 99–113, 1991.

    Article  Google Scholar 

  12. C Bron, and J Kerbosch: Finding all cliques of an undirected graph. Communications of the ACM, 16 (9): 575–577, 1973.

    Article  MATH  Google Scholar 

  13. F Buckley. Distance in Graphs. Addison-Wesley, Redwood City, Ca, 1990.

    Google Scholar 

  14. G A Carpenter. Neural network models for pattern recognition. Neural Networks, 2: 243–257, 1989.

    Article  Google Scholar 

  15. G A Carpenter. Neural network models for pattern recognition. In G A Carpenter and S Grossberg, editors, Pattern Recognition by Self-Organizing Neural Networks, pages 1–34. MIT Press, Cambridge, Ma, 1991.

    Google Scholar 

  16. G A Carpenter and S Grossberg. Pattern Recognition by Self-organizing Neural Networks. MIT Press, Cambridge, Ma, 1991.

    Google Scholar 

  17. V Cerny. Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45: 41–51, 1985.

    Article  MathSciNet  MATH  Google Scholar 

  18. C H Chen, editor. Pattern Recognition and Artificial Intelligence. Academic Press, New York, 1976.

    Google Scholar 

  19. N Chomsky: Syntactic Structures. Mouton, Hague, 6th edition, 1966.

    Google Scholar 

  20. N Chomsky, J P B Allen, and P Van Buren: Chomsky: Selected Readings. Oxford University Press, London-New York, 1971.

    Google Scholar 

  21. W F Clocksin, and C S Mellish: Programming in Prolog. Springer Verlag, Berlin-New-York-Tokyo, 1981.

    MATH  Google Scholar 

  22. Dasarathy 91] B V Dasarathy: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Comp. Society Press, Los Alamitos, Ca, 1991.

    Google Scholar 

  23. J E Dayhoff: Neural Network Architectures: An Introduction. Van Nostrand Reinhold, New York, 1990.

    Google Scholar 

  24. P A Devijver, and J Kittler: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    MATH  Google Scholar 

  25. P A Devijver, and J Kittler: Pattern Recognition Theory and Applications. Springer Verlag, Berlin-New York-Tokyo, 1986.

    Google Scholar 

  26. R O Duda, and P E Hart: Pattern Classification and Scene Analysis. John Wiley and Sons, New York, 1973.

    MATH  Google Scholar 

  27. R C Eberhart, and R W Dobbins: Neural Network PC Tools: A Practical Guide. Academic Press, San Diego, Ca, 1990.

    Google Scholar 

  28. S Even. Graph Algorithms. Computer Science Press, Rockville, Md, 1979.

    Google Scholar 

  29. Fischler and Eischlager 73] M A Fischler, and R A Elschlager: The representation and matching of pictorial structures. IEEE Transactions on Computers, C-22(1):67–92, 1973.

    Google Scholar 

  30. Foley 72] D H Foley. Consideration of sample and feature size. IEEE Transactions on Information Theory, IT-18(5):618–626, 1972.

    Google Scholar 

  31. K S Fu: Sequential Methods in Pattern Recognition and Machine Learning. Academic Press, New York, 1968.

    MATH  Google Scholar 

  32. K S Fu: Syntactic Methods in Pattern Recognition. Academic Press, New York, 1974.

    MATH  Google Scholar 

  33. K S Fu: Syntactic Pattern Recognition — Applications. Springer Verlag, Berlin, 1977.

    Book  MATH  Google Scholar 

  34. K S Fu: Picture syntax. In S K Chang and K S Fu, editors, Pictorial Information Systems, pages 104–127. Springer Verlag, Berlin, 1980.

    Chapter  Google Scholar 

  35. K S Fu: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    MATH  Google Scholar 

  36. Fukunaga 90] K Fukunaga: Introduction to Statistical Pattern Recognition. Academic Press, Boston, 2nd edition, 1990.

    Google Scholar 

  37. D Geman, S Geman, C Graffigne, and P Dong: Boundary detection by constrained optimisation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (7), 1990.

    Google Scholar 

  38. D E Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Ma, 1989.

    Google Scholar 

  39. R C Gonzalez, and M G Thomason: On the inference of tree grammars for pattern recognition. In Proceedings of the IEEE International Conference on System, Man and Cybernetics, pages 2–4. IEEE, 1974.

    Google Scholar 

  40. S Grossberg: Neural pattern discrimination. Journal of Theoretical Biology, 27: 291–337, 1970.

    Article  Google Scholar 

  41. S Grossberg: Neural pattern discrimination. In G A Carpenter and S Grossberg, editors, Pattern Recognition by Self-Organizing Neural Networks, pages 111–156. MIT Press, Cambridge, Ma, 1991.

    Google Scholar 

  42. R M Haralick, and G L Elliott: Increasing tree search efficiency for constraint satisfaction problems. In Proceedings of 6th IJCA I-79, pages 356–364, 1979.

    Google Scholar 

  43. F Harary: Graph Theory. Addison-Wesley, Reading, Ma, 1969.

    Google Scholar 

  44. P J Hayes: In defense of logic. In Proceedings of 5th IJCAI, Cambridge, Ma, 1977.

    Google Scholar 

  45. R Hecht-Nielsen: Neurocomputing. Addison-Wesley, Reading, Ma, 1990.

    Google Scholar 

  46. J J Hopfield, and D W Tank: Neural computation of decisions in optimization problems. Biological Cybernetics, 52: 141–152, 1985.

    MathSciNet  MATH  Google Scholar 

  47. J J Hopfield, and D W Tank: Computing with neural circuits: A model. Science, 233: 625–633, 1986.

    Article  Google Scholar 

  48. Johnson and Wichern 90] R A Johnson, and D W Wichern: Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ, 2nd edition, 1990.

    Google Scholar 

  49. J S Judd. Neural Network Design and the Complexity of Learning. MIT Press, Cambridge, Ma, 1990.

    Google Scholar 

  50. L Kaufman, and P J Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York, 1990.

    Book  Google Scholar 

  51. S Kirkpatrick, C D Gelatt, and M P Vecchi: Optimization by simulated annealing. Science, 220: 671–680, 1983.

    Article  MathSciNet  MATH  Google Scholar 

  52. Kohonen 89] T Kohonen: Self-Organization and Associative Memory. Springer Verlag, Berlin-New York-Tokyo, 3rd edition, 1989.

    Google Scholar 

  53. B Kosko: Adaptive bidirectional associative memories. In G A Carpenter and S Grossberg, editors, Pattern Recognition by Self-Organizing Neural Networks, pages 425–450. MIT Press, Cambridge, Ma, 1991.

    Google Scholar 

  54. R Kowalski: Logic for Problem Solving. North Holland, Amsterdam, 1979.

    MATH  Google Scholar 

  55. H T Lau: Algorithms on Graphs. TAB Professional and Reference Books, Blue Ridge Summit, Pa, 1989.

    Google Scholar 

  56. J MacQueen: Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium — 1, pages 281–297, 1967.

    Google Scholar 

  57. R J McEliece, E C Posner, E R Rodemich, and S S Venkatesh: The capacity of the Hopfield associative memory. IEEE Transactions on Information Theory, 33: 461, 1987.

    Article  MathSciNet  MATH  Google Scholar 

  58. J A McHugh: Algorithmic Graph Theory. Prentice-Hall, Englewood Cliffs, NJ, 1990.

    MATH  Google Scholar 

  59. L L McQuitty: Pattern-Analytic Clustering: Theory, Method, Research, and Configural Findings. University Press of America, Lanham, NY, 1987.

    Google Scholar 

  60. N Metropolis, A W Rosenbluth, M N Rosenbluth, A H Teller, and E Teller: Equation of state calculation by fast computing machines. Journal of Chemical Physics, 21: 1087–1092, 1953.

    Article  Google Scholar 

  61. R S Michalski, J G Carbonell, and T M Mitchell: Machine Learning I, II. Morgan Kaufmann Publishers, Los Altos, Ca, 1983.

    Google Scholar 

  62. Minsky 88] M L Minsky: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, Ma, 2nd edition, 1988.

    Google Scholar 

  63. R H Mohring, editor. Graph-Theoretic Concepts in Computer Science - 16th WG’90, Berlin-New York-Tokyo, 1991. Springer Verlag.

    Google Scholar 

  64. M C Mozer: The Perception of Multiple Objects: A Connectionist Approach. MIT Press, Cambridge, Ma, 1991.

    Google Scholar 

  65. M Nagl, editor: Graph-Theoretic Concepts in Computer Science - 15th WG ‘89, Berlin-New York-Tokyo, 1990. Springer Verlag.

    MATH  Google Scholar 

  66. Niemann 90] H Niemann: Pattern Analysis and Understanding. Springer Verlag, Berlin-New York-Tokyo, 2nd edition, 1990.

    Google Scholar 

  67. N J Nilsson: Problem Solving Methods in Artificial Intelligence. McGraw Hill, New York, 1971.

    Google Scholar 

  68. N J Nilsson: Principles of Artificial Intelligence. Springer Verlag, Berlin, 1982.

    Book  MATH  Google Scholar 

  69. E Oja. Subspace Methods of Pattern Recognition. Research Studies Press, Letchworth, England, 1983.

    Google Scholar 

  70. R H J M Otten, and L P P P van Ginneken: The Annealing Algorithm. Kluwer Academic Publishers, Norwell, Ma, 1989.

    Google Scholar 

  71. E A Patrick, and J M Fattu: Artificial Intelligence with Statistical Pattern Recognition. Prentice-Hall, Englewood Cliffs, NJ, 1986.

    Google Scholar 

  72. M Pavel. Fundamentals of Pattern Recognition. M. Dekker, New York, 1989.

    Google Scholar 

  73. T Pavlidis: Structural descriptions and graph grammars. In S K Chang and K S Fu, editors, Pictorial Information Systems, pages 86–103, Springer Verlag, Berlin, 1980.

    Chapter  Google Scholar 

  74. H Pospesel: Predicate Logic. Prentice-Hall, Englewood Cliffs, NJ, 1976.

    Google Scholar 

  75. C R Rao: Linear Statistical Inference and its Application. John Wiley and Sons, New York, 1965.

    Google Scholar 

  76. G J E Rawlins: Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, Ca, 1991.

    Google Scholar 

  77. H Reichgelt: Knowledge Representation: An AI Perspective. Ablex Publishing Corporation, Norwood, NJ, 1991.

    Google Scholar 

  78. S K Rogers, and M Kabrisky: An Introduction to Biological and Artificial Neural Networks for Pattern Recognition. SPIE, Bellingham, Wa, 1991.

    Google Scholar 

  79. H C Romesburg. Cluster Analysis for Researchers. Lifetime Learning Publications, Belmont, Ca, 1984.

    Google Scholar 

  80. R Rosenblatt: Principles of Neurodynamics. Spartan books, Washington, D.C., 1962.

    MATH  Google Scholar 

  81. A Rosenfeld: Picture Languages — Formal Models for Picture Recognition. Academic Press, New York, 1979.

    MATH  Google Scholar 

  82. D Rumelhart, and J McClelland: Parallel Distributed Processing. MIT Press, Cambridge, Ma, 1986.

    Google Scholar 

  83. D Schutzer: Artificial Intelligence, An Application-Oriented Approach. Van Nostrand Reinhold, New York, 1987.

    Google Scholar 

  84. Sedgewick 84] R Sedgewick: Algorithms. Addison-Wesley, Reading, Ma, 2nd edition, 1984.

    Google Scholar 

  85. L G Shapiro, and R M Haralick: Algorithms for inexact matching. In Proceedings 5th International Conference on Pattern Recognition, pages 202–207, IEEE Comp. Society Press, Los Alamitos, Ca, 1980.

    Google Scholar 

  86. M Sharples, D Hogg, C Hutchinson, S Torrance, and D Young: Computers and Thought, A Practical Introduction to Artificial Intelligence. The MIT Press, Cambridge, Ma, 1989.

    Google Scholar 

  87. G L Simons. Introducing Artificial Intelligence. NCC Publications, Manchester, 1984.

    Google Scholar 

  88. Simpson 90] P K Simpson: Artificial Neural Systems: Foundations

    Google Scholar 

  89. Paradigms, Applications, and Implementations. Pergamon Press, New York, 1990.

    Google Scholar 

  90. J Sklansky: Pattern Classifiers and Trainable Machines. Springer Verlag, New York, 1981.

    Book  MATH  Google Scholar 

  91. M Sonka: A new texture recognition method. Computers and Artificial Intelligence, 5 (4): 357–364, 1986.

    Google Scholar 

  92. H K Tan, S B Gelfand, and E J Delp: A cost minimization approach to edge detection using simulated annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (1), 1992.

    Google Scholar 

  93. J R Ullmann: An algorithm for subgraph isomorphism. Journal of the Association for Computing Machinery, 23 (1): 31–42, 1976.

    Article  MathSciNet  Google Scholar 

  94. P J M van Laarhoven: Theoretical and Computational Aspects of Simulated Annealing. Centrum voor Wiskunde en Informatik, Amsterdam, 1988.

    MATH  Google Scholar 

  95. P J M van Laarhoven, and E H L Aarts: Simulated Annealing: Theory and Applications. Dordrecht and Kluwer Academic Publisher, Norwell, Ma, 1987.

    Google Scholar 

  96. P D Wasserman: Neural Computing — Theory and Practice. Van Nostrand Rheinhold, New York, 1989.

    Google Scholar 

  97. H Wechsler: Computational Vision. Academic Press, London — San Diego, 1990.

    Google Scholar 

  98. P H Winston, editor. The Psychology of Computer Vision. McGraw Hill, New York, 1975.

    Google Scholar 

  99. Winston 84] P H Winston: Artificial Intelligence. Addison-Wesley, Reading, Ma, 2nd edition, 1984.

    Google Scholar 

  100. B Yang, W E Snyder, and G L Bilbro: Matching oversegmented 3D images to models using association graphs. Image and Vision Computing, 7 (2): 135–143, 1989.

    Article  Google Scholar 

  101. T Y Young, and T W Calvert: Classification, Estimation, and Pattern Recognition. American Elsevier, New YorkLondon-Amsterdam, 1974.

    Google Scholar 

  102. Z Zdrahal: A structural method of scene analysis. In Proceedings of IJCA I-81, pages 680–682, Vancouver, BC, Canada, 1981.

    Google Scholar 

  103. M Zeidenberg: Neural Network Models in Artificial Intelligence. E. Hoerwood, New York, 1990.

    Google Scholar 

  104. Y T Zhou: Artificial Neural Networks for Computer Vision. Springer Verlag, New York, 1992.

    Book  Google Scholar 

  105. H J Zimmermann, L A Zadeh, and B R Gaines: Fuzzy Sets and Decision Analysis. North Holland, Amsterdam-New York, 1984.

    Google Scholar 

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© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle

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Sonka, M., Hlavac, V., Boyle, R. (1993). Object recognition. In: Image Processing, Analysis and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3216-7_7

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  • DOI: https://doi.org/10.1007/978-1-4899-3216-7_7

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