Abstract
In recent years our very complex and technically oriented society has created a situation in which more people and organizations have become concerned with handling information and fewer with handling materials. The need for improved information systems has become more conspicuous, since the world is generating more information in its various forms and information is an essential element in decision making. One of the major problems in the design of modern information systems is automatic pattern recognition. This has been the subject of investigation by many diverse groups, including research workers dealing with electronic computers, automatic controls, information theory, applied physics, statistics, psychology, biology, physiology, medicine, and linguistics. Each group emphasizes certain aspects of the problem. This chapter attempts to discuss some engineering principles for the design of pattern recognition systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
K. Abend et al., Classification of Binary Random Patterns, IEEE Trans. on Information Theory IT-11, 538–544 (Oct. 1965)
N. Abramson, “Information Theory and Coding,” McGraw-Hill Book Co., New York (1963)
N. Abramson and D. Braverman, Learning to Recognize Patterns in a Random Environment, IRE Trans. on Information Theory IT-8 (5), 558–563 (Sept. 1962)
M. A. Aizerman et al., The Robbins-Monro Process and the Method of Potential Functions, Automatics and Telemechanics 26, 1951–1954 (1965)
M. A. Aizerman et al., Theoretical Foundations of the Potential Function Method in Pattern Recognition, Automatics and Telemechanics 25, 917–936 (1964)
M. A. Aizerman et al., The Probability Problem of Pattern Recognition Learning and the Method of Potential Functions, Automatics and Telemechanics 25, 1307–1323 (1964)
M. A. Aizerman et al., The Method of Potential Functions for the Problem of Restoring the Characteristic of a Function Converter from Randomly Observed Points, Automatics and Telemechanics 25, 1705–1714 (1964)
A. Albert, A Mathematical Theory of Pattern Recognition, Ann. Math. Stat. 34, 284–299 (March 1963)
L. Alt, Digital Pattern Recognition by Moments, in: “Optical Character Recognition” (G. L. Fisher et al., eds.), pp. 153–179, Spartan Books, Washington, D. C. (1962)
S. Amari, A Theory of Adaptive Pattern Classifiers, IEEE Trans. on Electronic Computers EC-16, 299–307 (June 1967)
T. W. Anderson and R. R. Bahadur, Classification into Two Multivariate Normal Distributions with Different Covariance Matrices, Ann. Math. Stat. 33 (1962)
T. W. Anderson, “Introduction to Multivariate Statistical Analysis,” John Wiley and Sons, New York (1958)
O. A. Bashkirov, E. M. Braverman and I. B. Muchnik, Potential Function Algorithms for Pattern Recognition Learning Machine, Automatics and Telemechanics 25, 692–695 (1964)
C. Blaydon, On a Pattern Classification Result of Aizerman, Braverman, and Rozonoer, IEEE Trans. on Information Theory IT-12, 83–83 (Jan. 1962)
W. W. Bledsoe, Further Results on the N-tuple Pattern Recognition Method, IRE Trans. on Electronic Computers EC-10 (1) p. 96 (March 1961)
W. W. Bledsoe and I. Browning, Pattern Recognition and Reading by Machine, in: “Proc. of the Eastern Joint Computer Conference” (1959), AIEE
H. D. Block, N. J. Nilsson, and R. O. Duda, Determination and Detection of Features in Pattern, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 75–110, Spartan Books, Washington, D. C. (1964)
J. A. Blum, Multidimensional Stochastic Approximation Procedures, Ann. Math. Stat. 6 (4) (1955)
E. M. Braverman and E. S. Pyatnitskii, Estimation of the Rate of Convergence of Algorithms Based on the Potential Function Method, Automatics and Telemechanics 27, 95–112 (1966)
E. M. Braverman, On the Method of Potential Functions, Automatics and Telemechanics 26, 2205–2213 (1965)
D. Braverman, Learning Filters for Optimum Pattern Recognition, IRE Trans. on Information Theory IT-8, 280–285 (July 1962)
D. B. Brick and J. Owen, A Mathematical Approach to Pattern Recognition and Self-Organization, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 137–168, Spartan Books, Washington, D. C. (1964)
D. T. Brown, A Note on Approximations to Discrete Probability Distributions, Information and Control 2, 386–392 (Dec. 1959)
J. A. Bradshaw, Letter Recognition Using a Captive Scan, IEEE Trans. Electronic Computers EC-12, p. 26 (Feb. 1963)
W. G. Chaplin and V. S. Levadi, A Generalization of the Linear Threshold Decision Algorithm to Multiple Classes, in “Computer and Information Sciences-II” (J. T. Tou, ed.), pp. 57–59, Academic Press, New York (1967)
Y. T. Chien and K. S. Fu, On Bayesian Learning and Stochastic Approximation, IEEE Trans. on Systems Science and Cybernetics SSC-3, 28–38 (June 1967)
27.Y. T. Chien and K. S. Fu, A Modified Sequential Recognition Machine Using Time-Varying Stopping Boundaries, IEEE Trans. on Information Theory IT-12 (2), 206–214 (April 1966)
C. K. Chow and C. N. Lin, An Approach to Structure Adaptation in Pattern Recognition, IEEE Trans. on Systems Science and Cybernetics SCC-2, 73–80 (Dec. 1966)
C. K. Chow, A Recognition Method Using Neighbor Dependence, IRE Trans. on Electronic Computers EC-11, 683–690 (Oct. 1962)
C. K. Chow, An Optimum Character Recognition System Using Decision Functions, IRE Trans. on Electronic Computers EC-6, 247–254 (Dec. 1957)
D. B. Cooper and P. W. Cooper, Nonsupervised Adaptive Signal Detection and Pattern Recognition, Information and Control 7, 416–444 (1964)
P. W. Cooper, Hyperplanes, Hyperspheres, and Hyperquadrics, as Decision Boundaries, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 111–138, Spartan Books, Washington, D. C. (1964)
P. W. Cooper, Some Topics in Nonsupervised Adaptive Detection for Multivariate Normal Distribution, in “Computer and Information Sciences-II” (J. T. Tou, ed.), pp. 123–146, Academic Press, New York (1967)
T. M. Cover and P. E. Hart, Nearest Neighbor Pattern Classification, IEEE Trans. on Information Theory IT-13 21–27 (Jan. 1967)
T. M. Cover, Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition, IEEE Trans. on Electronic Computers EC-14, 326–334 (June 1965)
W. E. Dickinson, A Character-Recognition Study, IBM J. Res. Dev. 4 (3) (July 1960)
G. P. Dinneen, Programming Pattern Recognition, in: “Proc. of the Western Joint Computer Conference” (1955), AIEE
R. O. Duda and H. Fossum, Pattern Classification by Iteratively Determined Linear and Piecewise Linear Discriminant Functions, IEEE Trans. on Electronic Computers EC-15, 220–232 (Apr. 1966)
A. Dvoretzky, On Stochastic Approximation, in “Proc. of the Third Berkeley Symposium on Mathematical Statistics and Probability,” Vol. 1 (1956)
M. Eden, Handwriting and Pattern Recognition, IRE Trans. on Information Theory IT-8, 160–171 (Feb. 1962)
E. A. Feigenbaum and J. Feldman (eds.), “Computers and Thought,” McGraw-Hill Book Co., New York (1963)
G. L. Fisher et al. (eds.), “Optical Character Recognition,” Spartan Books, Washington, D. C. (1962)
S. C. Fralick, Learning to Recognize a Pattern without a Teacher, IEEE Trans. on Information Theory IT-13, 57–64 (Jan. 1967)
K. S. Fu et al., A Dynamic Programming Approach to Sequential Pattern Recognition, IEEE Trans. on Electronic Computers EC-16, 790–803 (Dec. 1967)
K. S. Fu, A Sequential Decision Model for Optimum Recognition, in “Biological Prototypes and Synthetic Systems,” Vol. I (E. E. Bernard and M. R. Kare, eds.), Plenum Press, New York (1962)
A. Gamba, New Developments in Artificial Intelligence and Pattern Recognition, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 219–229, Spartan Books, Washington, D. C. (1964)
V. E. Giuliano et al., Automatic Pattern Recognition by a Gestalt Method, Information and Control 4 (4) (Dec. 1961)
E. C. Greanias et al., The Recognition of Handwritten Numerals by Contour Analysis, IBM J. Res. Dev. 7, 14–21 (Jan. 1963)
J. S. Griffin et al., A Pattern Identification Device Using Linear Decision Functions, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 167–193, Spartan Books, Washington, D. C. (1964)
R. L. Grimsdale et al., A System for the Automatic Recognition of Patterns, Proc. IEEE (London) 106, Part B, No. 26, 210–221 (March 1959)
J. K. Hawkins, Self-organizing Systems-A Review and Commentary, Proc. IRE 49, 31–48 (Jan. 1961)
W. H. Highleyman, Linear Decision Functions, with Application to Pattern Recognition, Proc. IRE 50, 1501–1514 (June 1962)
W. S. Holmes, H. R. Leland, and G. E. Richmond, Design of a Photo Interpretation Automaton, “Proc. of the Fall Joint Computer Conference,” Vol. 22 (1962), AFIP, The National Press, Palo Alto, Calif
L. P. Horowitz and G. L. Shelton, Pattern Recognition Using Autocorrelation, Proc. IRE Jan. 1961
L. A. Kamentsky and C. N. Liu, A Theoretical and Experimental Study of a Model for Pattern Recognition, in “Computer and Information Sciences-I” (J. T. Tou and R. H. Wilcox, eds.), pp. 194–218, Spartan Books, Washington, D. C. (1964)
L. A. Kamentsky and C. N. Liu, Computer-Automated Design of Multifont Print Recognition Logic, IBM J. Res. Dev. 7, 2–13 (Jan. 1963)
L. A. Kamentsky, Pattern and Character Recognition Systems-Picture Processing by Nets of Neuron-Like Elements, in “Proc. of the Western Joint Computer Conference,” (1959), pp. 304–309, AIEE
L. N. Kanal and N. C. Randall, “Recognition System Design by Statistical Analysis, in “Proc. of the 19th National Conference,” ACM Publication P-64, Aug. 1964
L. Kanal, “Evaluation of a Class of Pattern Recognition Networks, in “Biological Prototypes and Synthetic Systems,” Vol. I, (E. E. Bernard and M. R. Kare, eds.), pp. 261–269, Plenum Press, New York (1962)
H. Kazmierczak and K. Steinbuch, Adaptive Systems in Pattern Recognition, IEEE Trans. on Electronic Computers EC-12, 822–835 (Dec. 1963)
D. G. Keehn, A Note on Learning for Gaussian Properties, IEEE Trans. on Information Theory Jan. 1965
J. Kiefer and J. Wolfowitz, Stochastic Estimation of the Maximum of a Regression Function, Ann. Math. Stat. 23 (3) (1952)
J. S. Koford and G. F. Groner, The Use of an Adaptive Threshold Element to Design a Linear Optimal Pattern Classifier, IEEE Trans. on Information Theory IT-12, 42–50 (Jan. 1966)
V. A. Kovalevsky, Present and Future of Pattern Recognition Theory, in “Proc. IFIP Congress 65,” Vol. I, pp. 37–43, Spartan Books, Washington D. C. (1966)
L. S. G. Kovasznay, and H. M. Joseph, Image Processing, Proc. IRE, 43, 560–570 (Mar 1955)
J. J. Leimer, Design Factors in the Development of an Optical Character Recognition System, IRE Trans. on Information Theory IT-8, 161–171 (Feb. 1962)
P. M. Lewis, The Characteristic Selection Problem in Recognition Systems, IRE Trans. on Information Theory IT-8, 171–178 (Feb. 1962)
P. M. Lewis, Approximating Probability Distributions to Reduce Storage Requirements, Information and Control 2, 214–225 (1959)
C. N. Liu, A Programmed Algorithm for Designing Multifont Character Recognition Logic, IEEE Trans. on Electronic Computers EC-13, 586–593 (Oct. 1964)
N. V. Loginov, Methods of Stochastic Approximation, Automatics and Telemechanics 27, 185–204 (1966)
T. Marill and D. M. Green, On the Effectiveness of Receptors in Recognition Systems, IEEE Trans. on Information Theory IT-9, 11–17 (Jan. 1963)
T. Marill, Automatic Recognition of Speech, IRE Trans. on Human Factors Electronics HFE-2, 34–38 (Mar. 1961)
T. Marill and D. M. Green, Statistical Recognition Functions and the Design of Pattern Recognizers, IRE Trans. on Electronic Computers EC-9, 472–477 (Dec. 1960)
R. L. Mattson and J. E. Damman, A Technique for Determining and Coding Subclasses in Pattern Recognition Problems, IBM J. Res. Dev. 9 (4) (July 1965)
M. Minsky, Steps Toward Artificial Intelligence, Proc. IRE 49, 8–30 (Jan. 1961)
M. Nadler, An Analog-Digital Character Recognition System, IEEE Trans. on Electronic Computers EC-12, 814–821 (Dec. 1963)
Z. J. Nikolic and K. S. Fu, An Algorithm for Learning Without External Supervision and Its Application to Learning Control Systems, IEEE Trans. on Automatic Control AC-11, 414–422 (July 1966)
N. J. Nilsson, “Learning Machines,” McGraw-Hill Book Co., New York (1965)
A. B. J. Novikoff, Integral Geometry as a Tool in Pattern Perception, in “Principles of Self-Organization,” (H. Von Foerster and G. W. Zopf, Jr., eds.), Pergamon Press, New York, (1962)
F. M. Reza, “An Introduction to Information Theory,” McGraw-Hill Book Co., New York (1961)
H. Robbins, and S. Monro, A Stochastic Approximation Method, Ann. Math. Stat. 22 (1) (1951)
F. Rosenblatt, “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms,” Spartan Books, Washington, D. C. (1961)
A. Rosenfeld, An Approach to Automatic Photographic Interpretation, Photogrammetric Engineering 27, pp. 660–665 (Sept. 1962)
G. S. Sebestyen, and J. Edie, An Algorithm for Nonparametric Pattern Recognition, IEEE Trans. on Electronic Computers EC-15, 908–915 (Dec. 1966)
G. S. Sebestyen, Pattern Recognition by an Adaptive Process of Sample Set Construction, IRE Trans. on Information Theory IT-8, 582–591 (Sept. 1962)
G. S. Sebestyen, “Decision Making Processes in Pattern Recognition,” The Macmillan Company, New York (1962)
O. G. Selfridge, Pattern Recognition and Learning, in “Information Theory” (C. Cherry, ed.), Butterworth Scientific Publications, London (1956)
H. Sherman, A Quasi-Topological Method for Machine Recognition of Line Patterns, in “Proc. International Conference on Information Processing,” Paris, 1959, Butterworth Scientific Publications, London (1960)
D. F. Specht, Generation of Polynomial Discriminant Functions for Pattern Recognition, IEEE Trans. on Electronic Computers EC-16, 308–319 (June 1967)
R. J. Spinrad, Machine Recognition of Hand Printing, Information and Control 8, 124–142 (Apr. 1965)
J. Spragins, A Note on the Iterative Application of Bayes’ Rule, IEEE Trans. on Information Theory IT-11, 544–549 (1965)
W. Sprick and K. Ganzhorn, An Analogous Method for Pattern Recognition by Following the Boundary, in “Proceedings of the International Conference on Information Processing,” Paris, 1959, Butterworth Scientific Publications, London (1960)
S. D. Stearns, A Method for the Design of Pattern Recognition Logic, IRE Trans. on Electronic Computers, EC-9, 48–53 (March 1960)
G. P. Steck, Stochastic Model for the Browning-Bledsoe Pattern Recognition Scheme, IRE Trans. on Electronic Computers EC-11, 274–282 (Apr. 1962)
D. M. Stern and D. W. C. Shen, Character Recognition by Context-Dependent Transformations, Proc. IEEE (British) III (11) (Nov. 1964)
Ya. Z. Tsypkin, Optimization, Adaptation, and Learning in Automatic System, in “Computer and Information Sciences-II” (J. T. Tou, ed.), pp. 15–31, Academic Press, New York (1967)
Ya. Z. Tsypkin, Establishing Characteristics of a Function Transformer from Randomly Observed Points, Automatics and Telemechanics 26, 1947–1950 (1965)
J. T. Tou, Feature Extraction in Pattern Recognition, in “Pattern Recognition,” Vol. I, Pergamon Press, New York (1968)
J. T. Tou, Information Theoretic Approach to Pattern Recognition, IEEE International Convention Record (1968)
J. T. Tou, (ed.) “Computer and Information Scienees-II,” Academic Press, New York (1967)
J. T. Tou and R. P. Heydorn, Some Approaches to Optimum Feature Extraction, in “Computer and Information Sciences-II” (J. T. Tou, ed.), pp. 57–59, Academic Press, New York (1967)
J. T. Tou and R. H. Wilcox (eds.), “Computer and Information Sciences-I,” Spartan Books, Washington, D. C. (1964)
L. Uhr, “Pattern Recognition,” John Wiley and Sons, New York, (1966)
S. H. Unger, Pattern Detection and Recognition, Proc. IRE 47, 1737–1752
S. Watanabe et al., Evaluation and Selection of Variables in Pattern Recognition, in “Computer and Information Sciences-II” (J. T. Tou, ed.), pp. 92–121, Academic Press, New York
S. Watanabe (ed.), “Methodologies in Pattern Recognition,” Academic Press, New York
B. Widrow, Generalization and Information Storage in Networks of Adaline Neurons, in “Self-Organizing Systems” (M. C. Yovits et al., eds.) Spartan Books, Washington, D. C. (1962)
R. O. Winder, The Fundamentals of Threshold Logic, in “Applied Automata Theory” (J. T. Tou, ed.), Academic Press, New York (1968)
M. C. Yovits et al. (eds.), “Self-Organizing Systems,” Spartan Books, Washington, D. C. (1962)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1969 Plenum Press
About this chapter
Cite this chapter
Tou, J.T. (1969). Engineering Principles of Pattern Recognition. In: Tou, J.T. (eds) Advances in Information Systems Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-9050-7_4
Download citation
DOI: https://doi.org/10.1007/978-1-4615-9050-7_4
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4615-9052-1
Online ISBN: 978-1-4615-9050-7
eBook Packages: Springer Book Archive