Overview
Machine pattern recognition is a very valuable technique for distinguishing objects and signals in intelligent automation. However, when considered in depth, pattern recognition becomes very complex mathematically. Thus, we provide here an introduction only, aimed chiefly at newcomers. References to textbooks and papers providing more detailed information appear at the end of this chapter.
The chapter is organized as follows. We begin by explaining the nature and terminology of pattern recognition, including the measures used to specify performance. Then the three approaches to PR in common use are introduced, and two (heuristic and feature space methodologies) are explained in further detail. Finally, advice is offered for selecting and assessing the applicability of pattern recognition methodology for specific problems.
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References
D.A Bell, in: Pattern Recognition—Ideas in Practice, B. Batchelor (ed.), Chapter 5, Plenum Press, New York (1978).
C.B. Chittineni, Efficient feature subset selection with probabilistic distance criteria, Inf. Sciences 22, 19–35 (1980).
P.A. Lachenbruch and M.R. Mickey, Estimation of error rates in discriminant analysis, Technometrics 10(1), 1–10 (1968).
V. Popovici, Application of syntactic pattern recognition to defect classification, Ph.D. thesis, The City University, London (1976).
K. Nakada, Y. Nakano, and Y. Uchikura, Recognition of Chinese characters, Inst. of Physics Conf Pub. 13, 45–52 (1972).
D.C. Gonzalez and M.G. Thomason, Syntactic Pattern Recognition—an Introduction, Addison-Wesley, Reading, Mass. (1978).
K.S. Fu (ed.), Syntactic Pattern Recognition—Applications, Vol. 14 of series “Communications and Cybernetics,” Springer, New York (1977).
R.O. Duda and P.E. Hart, Pattern Recognition and Scene Analysis, Wiley, New York (1973).
P. Devijver and J. Kittler, Pattern Recognition: a Statistical Approach, Prentice-Hall, New Jersey (1982).
D.J. Hand, Kernal Discriminant Analysis, Wiley, New York (1982).
W.J. Hill, Defect recognition in automated surface inspection, Ph.D. thesis, The City University, London (1977).
J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles (Sect. 5.3.3), Addison-Wesley, Reading, Mass. (1974).
W.J. Wee, Generalised inverse approach to adaptive multiclass pattern classification, IEEE Trans. Comps. C-17(17), 1157–1164 (Dec, 1968).
A. Wald, Sequential Analysis, Dover, New York (1973).
P.G. Hoel, Introduction to Mathematical Statistics, 4th ed., Wiley, New York, Chap. 13 (1971).
J. Raviv, Decision making in Markov chains applied to the problem of pattern recognition, IEEE Trans. Information Theory, Vol. IT-17, No.4, 536–551 (Oct. 1967).
C.B. Chittineni, Signal classification for automatic industrial inspection, IEE Proc., Vol. 129, Pt.E, No.3, 101–106 (May 1982).
P. Devijver and J. Kittler, Pattern recognition—A statistical approach, Chap. 3, Prentice-Hall, New Jersey (1982).
J.W. Sammon, A Non-Linear mapping for data structure analysis, IEEE Trans., Comp. Vol. C-18, 401–409 (1969).
L Norton-Wayne, A coding approach to pattern recognition in J. Kittler, K.S. Fu, and L.F. Pau (eds.), Pattern Recognition Theory and Applications, Reidel, Dordrecht (1982).
L. Kanal, Patterns in pattern recognition, 1968–74, IEEE IT-20(6), Nov. (1974).
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© 1986 Springer Science+Business Media New York
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Browne, A., Norton-Wayne, L. (1986). Introduction to Machine Pattern Recognition. In: Vision and Information Processing for Automation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-2028-7_3
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DOI: https://doi.org/10.1007/978-1-4899-2028-7_3
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