Advertisement

The “Rubber-Mask” Technique-II, Pattern Storage and Recognition

  • Bernard Widrow

Abstract

This paper briefly summarizes much of the work in pattern recognition to date, and relates the rubber mask technique to previous work. A scheme for incorporating flexible-mask methods into a proposed pattern recognition and memory system is presented. A discussion based on some facts and on some conjecture of the human eye/brain system and how it recognizes patterns, possibly by flexible matching, is also presented.

Keywords

Pattern Recognition Facial Recognition Polar Bear Optical Character Recognition Integral Geometry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    B. Widrow, “The rubber mask” technique I: Pattern measurement and analysis,” Pattern Recognition 5, 175–197 (1973).CrossRefGoogle Scholar
  2. 2.
    C.J.W. Mason, “Pattern Recognition Bibliography”, IEEE Systems, Man, and Cybernetics, Group Newsletter, October 1970, December 1970, February 1971, June 1971.Google Scholar
  3. 3.
    R.L. Gregory, “Eye and Brain: The Psychology of Seeing”, World University Library. McGraw-Hill, New York (1966).Google Scholar
  4. 4.
    Police identification kits are manufactured by the Identi-Kit Company which claims for its product in an advertisement on page 39 of Law and Order, September 1972:…It allows you to go to the witness or victim of a crime and develop a composite facial likeness of a suspect within minutes. Manual overlay of six to nine facial components becomes a complete line drawing without the use of complicated machines, photographs or the services of an artist. Also, Identi-Kit composites are easily reproduced and automatically code themselves for instant transmission to other Identi-Kit users all over the world….Google Scholar
  5. 5.
    B. Julesz, Foundations of Cyclopean Perception. University of Chicago Press, Chicago (1971).Google Scholar
  6. 6.
    Plato’s Republic, Book VI, Jowett Translation, Random House, Vintage Paperbacks.Google Scholar

Adaline/Madaline

  1. 1.
    B. Widrow and M.E. HOFF, “Adaptive switching circuits”, WESCON Convention Ree., Institute of Radio Engineers, N.Y. Pt. 4, pp. 96–104 (1960).Google Scholar
  2. 2.
    B. Widrow, “Generalization and information storage in networks of Adaline ‘neurons’,” Self Organizing Systems, M.C. Yovitz, G.T. Jacobi and G.D. Goldstein (eds) pp. 435–461. Spartan, Washington, D.C. 1962.Google Scholar
  3. 3.
    K. Steinbuch and B. Widrow, “A critical comparison of two kinds of adaptive classification networks”, IEEE Trans. Electronic Computers EC-14 (5), 737–740 (1965)CrossRefGoogle Scholar
  4. 4.
    J.S. Koford and G.F. Groner, “The use of an adaptive threshold element to design a linear optimal pattern classifier,”IEEE Trans. Information Theory IT-12, 42–50 (1966)CrossRefGoogle Scholar
  5. 5.
    C.H. Mays, “Effects of adaptation parameters on convergence time and tolerance for adaptive threshold elements”, IEEE Trans. Electronic Computers (short notes) EC-13, 465–468 (1964).CrossRefGoogle Scholar
  6. 6.
    N.J. Nilsson, Learning Machines. McGraw-Hill, New York (1965).Google Scholar
  7. 7.
    B. Widrow et al., “Adaptive antenna systems,” Proc. IEEE 55, 2143–2159 (1967).CrossRefGoogle Scholar

Perceptron

  1. 1.
    F. Rosenblatt, “The perceptron, a theory of statistical separability in cognitive systems”, Report VG-1196-G-1, Cornell Aeronautical Lab., Buffalo, New York (1958).Google Scholar
  2. 2.
    F. Rosenblatt, Principles of Neurodynamics, and the Theory of Brain Mechanisms. Spartan Books, Washington, P.C. (1962).Google Scholar
  3. 3.
    H.P. Block, “The perceptron: A model for brain functioning”. I, Rev. Mod. Phys. 34 (1), 123–135 (1962).CrossRefGoogle Scholar
  4. 4.
    H.P. Block, B.W. Knight and F. Rosenblatt, “Analysis of a four- layer series-coupled perceptron.” II, Rev. Mod. Phys. 34 (1), 135–142 (1962).CrossRefGoogle Scholar
  5. 5.
    N.J. Nilsson, Learning Machines. McGraw-Hill, New York (1965).Google Scholar
  6. 6.
    G. Nagy, “The state of art in pattern recognition”, Proc. IEEE 56 (5), 836–862 (1968).CrossRefGoogle Scholar
  7. 7.
    M.L. Minsky and S. Papert, Perceptrons: an Introduction to Computational Geometry. M.I.T. Press, Cambridge, Mass. (1969).Google Scholar

Matched Filters and Learning Matrices

  1. 1.
    “Matched filter issue”, IRE Trans. Information Theory IT-6, 309–417 (1960).Google Scholar
  2. 2.
    T. KaiLath, “Adaptive matched filters”, Mathematical Optimization Techniques, R. Bellman (ed), pp. 109–140. University of California Press (1963).Google Scholar
  3. 3.
    K. Steinbuch and U.A.W. Piske, “Learning matrices and their applications”, IEEE Trans. Electronic Computers EC-12 (5), 846–862 (1963).CrossRefGoogle Scholar
  4. 4.
    H. Kazmierczak and K. Steinbuch, “Adaptive systems in pattern recognition,” IEEE Trans. Electronic Computers EC-12 (5), 822–835 (1963).CrossRefGoogle Scholar

Linear and Polynomial Discriminants

  1. 1.
    W.H. Highleyman, “Linear decision functions, with application to pattern recognition”, Proc. IRE 50, 1501–1514 (1962).CrossRefGoogle Scholar
  2. 2.
    T.W. Anderson, Introduction to Multivariate Statistical Analysis. Wiley, New York (1965).Google Scholar
  3. 3.
    J.S. Koford and G.F. Groner,”The use of an adaptive threshold element to design a linear optimal pattern classifier”, IEEE Trans. Information Theory IT-12, 42–50 (1966).CrossRefGoogle Scholar
  4. 4.
    D. F. Specht, “Generation of polynomial discriminant-functions for pattern recognition”, IEEE Trans. Electronic Computers EC-16 (1), 308–319 (1967).CrossRefGoogle Scholar
  5. 5.
    D.F. Specht, “Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition”, IEEE Trans. Biomedical Electronics BME-14 (2), 90–95 (1967)CrossRefGoogle Scholar

Nearest Neighbour Rules

  1. 1.
    T.M. Cover and P. E. Hart, “Nearest neighbor pattern classification”, IEEE Trans. Information Theory IT-13 (1), 21–27 (1967).CrossRefGoogle Scholar
  2. 2.
    T.M. Cover, “Estimation by the nearest neighbor rule”, IEEE Trans. Information Theory IT-14 (1), 50–55 (1968).CrossRefGoogle Scholar

Integral Geomatry

  1. 1.
    L.A. Santalo, Introduction to Integral Geometry. Hermann, Paris (1953).Google Scholar
  2. 2.
    A.B.J. Novikoff, “Integral geometry as a tool in pattern perception”, Principles of Self-Organization, Von FORESTER and Zopf (eds), pp. 347–368, Pergamon, Oxford (1962).Google Scholar
  3. 3.
    G. Tenery, “Pattern recognition function of integral geometry”, IEEE Trans. Military Electronics MIL-7, 196–199 (1963).CrossRefGoogle Scholar
  4. 4.
    E. Wong and J.A. Steppe, “Invariant recognition of geometrical shapes”, Methodologies of Pattern Recognition, S. Watanabe (ed), pp. 535–543. Academic, New York (1969).Google Scholar

Pattern Classification by Clustering/Learning without a teacher

  1. 1.
    F. Mosteller, “On pooling data”, J. Am. Stat. Assoc. 43 (242), 231–242 (1948).CrossRefGoogle Scholar
  2. 2.
    T.T. Tanimoto and D.J. Rogers, “A computer program for classifying plants”, Science 132 (3434), 1115–1118 (1960).CrossRefGoogle Scholar
  3. 3.
    D.B. Cooper and P.W. Cooper, “Nonsupervised adaptive signal detection and pattern recognition”, Inform. Control 7 (3), 416–444 (1964).CrossRefGoogle Scholar
  4. 4.
    R.E. Bonner, “On some clustering techniques”, IBM J. Res. Dev. 8 (1), 22–32 (1964).CrossRefGoogle Scholar
  5. 5.
    R.L. Mattson and J.E. Dammann, “A technique for determining and coding subclasses in pattern recognition problems”, IBM J. Res. Dev. 9 (4), 294–302 (1965).CrossRefGoogle Scholar
  6. 6.
    G.H. Ball and D.J. Hall, “Isodata, a novel method of data analysis and pattern classification”, Stanford Research Institute, Menlo Park, Calif. (April 1965).Google Scholar
  7. 7.
    J. Spragins, “Learning without a teacher”, IEEE Trans. Information Theory IT-12 (2), 223–230 (1966).CrossRefGoogle Scholar
  8. 8.
    S.C. Fralick, “Learning to recognize patterns without a teacher”, IEEE Trans. Information Theory IT-13 (1), 57–64 (1967).CrossRefGoogle Scholar
  9. 9.
    W.C. Miller, “A modified mean square error criterion for use in unsupervised learning”, Stanford Electronics Laboratories, Stanford, Calif., Tech. Rept. SEL-67–066 (TR 6778–2) (August 1967).Google Scholar
  10. 10.
    YA.Z. Tsypkin, “Self learning—what is it?” IEEE Trans. Automatic Control AC13 (6), 608–612 (1968).CrossRefGoogle Scholar
  11. 11.
    C.G. Hilborn, Jr. and D.G. Lainiotis, “Unsupervised learning minimum risk pattern classification for dependent hypotheses and dependent measurements”, IEEE Trans. Systems Science and Cybernetics SSC-5 (2), 109–115 (1969).CrossRefGoogle Scholar
  12. 12.
    A.N. Mucciardi and E.E. Gose, “An automatic algorithm and its properties in high-dimensional spaces,” IEEE Trans. Systems, Man and Cybernetics SMC-2 (2), 247–254 (1972).CrossRefGoogle Scholar

Linguistic Approach to Pattern Recognition

  1. 1.
    R.A. Kirsch, “Computer interpretation of English text and picture patterns”, IEEE Trans. Electronic Computers EC-13, 363–376 (1964).CrossRefGoogle Scholar
  2. 2.
    R.S. Ledley, “High-speed automatic analysis of biomedical pictures”, Science 146, 216–223 (1964).CrossRefGoogle Scholar
  3. 3.
    N. Chomsky, Aspects of the Theory of Syntax. M.I.T. Press (1965).Google Scholar
  4. 4.
    R. Narasimhan, “Syntax-directed interpretation of classes of pictures”, Comm. ACM 9, 166–173 (1966).CrossRefGoogle Scholar
  5. 5.
    M.B. Clowes, “Transformational grammars and the organization of pictures”, Automatic Interpretation and Classification of Images, A. Grasselli (ed), pp. 43–76. Academic, New York (1969).Google Scholar
  6. 6.
    R. Narasimhan, “On the description, generation, and recognition of classes of pictures”, Automatic Interpretation and Classification of Images, A. Grasselli (ed), pp. 1–41. Academic, New York (1969).Google Scholar
  7. 7.
    M. Eden, “Handwriting generation and recognition”, Recognizing Patterns, P.A. Kolers and M. Eden (eds) pp. 138–154. M.I.T. Press, Cambridge, Mass. (1968).Google Scholar
  8. 8.
    W.F. Miller and A.C. Shaw,”Linguistic methods in picture processing—a survey”, Fall Joint-Computer Conference, pp. 279–290 (December 1968).Google Scholar
  9. 9.
    A. Rosenfeld, “Chapter on picture description and picture languages,” Picture Processing by Computer, pp. 167–184. Academic, New York (1969).Google Scholar
  10. 10.
    J. Hopcroft and J. Ullman, Formal Languages and their Relation to Automata. Addison-Wesley, Reading, Mass. (1969).Google Scholar
  11. 11.
    A.C. Shaw, “Parsing of graph-representable pictures; J. ACM 17 (3), 453–481 (1970).CrossRefGoogle Scholar
  12. 12.
    H.C. Lee and K.S. FU, “A stochastic syntax analysis procedure and its application to pattern recognition,” IEEE Trans. Comp., (July 1972).Google Scholar
  13. 13.
    “Special Issue on Syntactic Pattern Recognition”, Pattern Recognition 4, No. 1 (January 1972).Google Scholar

Fingerprint Recognition

  1. 1.
    William DIENSTEIN, Technics for the Crime Investigator. Charles C. Thomas (1952).Google Scholar
  2. 2.
    A. Grasselli, “On the automatic classification of fingerprints”, Methodologies of Pattern Recognition, S. Watanabe (ed.), pp. 253–315. Academic, New York (1969).Google Scholar
  3. 3.
    L.S. Penrose, “Dermatoglyphics”, Scient, Am. pp. 72–84 (December 1969).Google Scholar
  4. 4.
    “Proc. 1st and 2nd Natl. Symp. Law Enforcement Science and Technology” Thompson/Academic Press and IITRI. ArticlesGoogle Scholar
  5. (a).
    John E. GAFFNEY, JR. et al., “Fingerprint encoding and matching procedures for automated recognition systems”Google Scholar
  6. (b).
    C.B. Shelman, “Machine extraction of ridge endings from a noisy fingerprint”, pp. 409–416.Google Scholar
  7. (c).
    Vincent V. Horvath and Charles D. Doyle, “Recent developments in fingerprint recognition using coherent optical processing”, pp. 417–426.Google Scholar
  8. (d).
    Bernard M. Van Emden, “Advanced computer based fingerprint automatic classification technique” (FACT), pp. 493–505.Google Scholar
  9. (e).
    M.D. Freedman and E.D. Hietanen, “Application of parallel neighborhood logic to fingerprint processing”, pp. 507–509.Google Scholar
  10. (f).
    Richard W. Schwartz, “System considerations in automated fingerprint classification”, pp. 511–515.Google Scholar

Human Vision

  1. 1.
    R.L. Gregory, Eye and Brain, the Psychology of Seeing. World University Library, McGraw-Hill, New York (1966).Google Scholar
  2. 2.
    E.R. Hilgard and G.H. Bower, Theoriesof Learning, 3rd ed. Appleton-Century-Crofts, New York (1966).Google Scholar
  3. 3.
    P.C. Dodwell, Visual Pattern Recognition. Holt, Rinehart and Winston, New York (1970).Google Scholar
  4. 4.
    T.N. Cornsweet, “Measuring movements of the retinal image with respect to the retina”, Biomedical Sciences Instrumentation, W.E. Murray and P.F. Salisbury (ed), vol. 2. Plenum, New York (1964).Google Scholar
  5. 5.
    N.H. Mackworth, “A stand camera for line-of-night recording”, Perception and Psychophysics 2 (1967).Google Scholar
  6. 6.
    N.H. Mackworth and J.S. Bruner, “How adults and children search and recognize pictures”, Human Development 13, 149–177 (1970).CrossRefGoogle Scholar

Books on Pattern Recognition

  1. 1.
    G.L. Fischer, Jr., D.K. Pollock, B. Radack and M.E. Stevens (eds), Optical Character Recognition. Spartan Books, Washington (1962).Google Scholar
  2. 2.
    G. Sebestyen, Decision-Making Process in Pattern Recognition. Macmillan, New York (1962).Google Scholar
  3. 3.
    D.K. Pollock, C.J. Koester and J.T. Tippett, Optical Processing of Information. Spartan Books, Washington, D.C. (1963).Google Scholar
  4. 4.
    N.J. Nilsson, Learning Machines. McGraw-Hill, New York (1965).Google Scholar
  5. 5.
    L. Uhr, Pattern Recognition. Wiley, New York (1966).Google Scholar
  6. 6.
    K.S. Fu, Sequential Methods in Pattern Recognition and Machine Learning. Academic, New York (1968).Google Scholar
  7. 7.
    L.N. Kanal (ed), Pattern Recognition. Thompson, Washington (1968).Google Scholar
  8. 8.
    A. Rosenfeld, Picture Processing by Computer. Academic, New York (1969).Google Scholar
  9. 9.
    M.S. Watanabe, Methodologies of Pattern Recognition. Academic, New York (1969).Google Scholar
  10. 10.
    A. Grasselli (ed), Automatic Interpretation and Classification of Images. Academic, New York (1969).Google Scholar
  11. 11.
    J. Mendel and K. S. Fu, Adaptive Learnings and Pattern Recognition: Theory and Applications. Academic, New York (1970).Google Scholar
  12. 12.
    N. Bongard, Pattern Recognition. Spartan Books, New York (1970).Google Scholar
  13. 13.
    YA. Z. Tsypkin, Adaptation and Learning in Automatic Systems. Academic, New York (1971).Google Scholar
  14. 14.
    K.S. Fu (ed) Pattern Recognition and Machine Learning. Plenum, New York (1971).Google Scholar
  15. 15.
    O. Grusser and R. Klinke (eds), Pattern Recognition in Biological and Technical Systems. Springer, Berlin, New York(1971).Google Scholar
  16. 16.
    K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic, New York (1972).Google Scholar

Optical Character Recognition(OCR)

  1. 1.
    G.L. Fischer, Jr., D.K. Pollock, B. Radack and M.E. Stevens (eds), Optical Character Recognition. Spartan Books, Washington, D.C. (1962).Google Scholar
  2. 2.
    C.N. Liu and G.L. Shelton, JR., “An experimental investigation of a mixed-font print recognition system” IEEE Trans. Computers EC-15 (6), 916–925 (1966).CrossRefGoogle Scholar
  3. 3.
    G. Nagy, “Preliminary investigation of techniques for automated reading of unformatted text,” IBM Res. RC 1867, York-town Heights, New York (30 June 1967)Google Scholar
  4. 4.
    T. Trickett, “The design of a standard type font for optical character recognition,” Honeywell Comput. Jl. 3 (1), 2–11 (1969).Google Scholar
  5. 5.
    “Special issue on Optical Character Recognition”, Pattern Recognition 2, No. 3 (September 1970).Google Scholar
  6. 6.
    J.R. Parks, “A multi-level system of analysis for mixed font and hand-blocked printed character recognition” Automatic Interpretation and Classification of Images, A. Grasselli (ed), pp. 295–322. Academic, New York (1969).Google Scholar

Copyright information

© Plenum Press, New York 1974

Authors and Affiliations

  • Bernard Widrow
    • 1
  1. 1.Stanford Electronics LaboratoriesStanfordUSA

Personalised recommendations