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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
A P H Ambler: A versatile system for computer controlled assembly. Artificial Intelligence, 6 (2): 129–156, 1975.
D J Amit: Modeling Brain Function: The World of Attractor Neural Networks. Cambridge University Press, Cambridge, England; New York, 1989.
D H Ballard and C M Brown. Computer Vision. Prentice-Hall, Englewood Cliffs, NJ, 1982.
A Barrero: Inference of tree grammars using negative samples. Pattern Recognition, 24 (1): 1–8, 1991.
H G Barrow and R J Popplestone. Relational descriptions in picture processing. Machine Intelligence, 6, 1971.
R Beale, and T Jackson: Neural Computing — An Introduction. Adam Hilger, Bristol, 1990.
Berge 76] C Berge. Graphs and Hypergraphs. American Elsevier, New York, 2nd edition, 1976.
J R Bittner, and E M Reingold: Backtrack programming techniques. Communications of the ACM, 18 (11): 651–656, 1975.
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.
C Bouman, and B Liu: Multiple resolution segmentation of textured images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (2): 99–113, 1991.
C Bron, and J Kerbosch: Finding all cliques of an undirected graph. Communications of the ACM, 16 (9): 575–577, 1973.
F Buckley. Distance in Graphs. Addison-Wesley, Redwood City, Ca, 1990.
G A Carpenter. Neural network models for pattern recognition. Neural Networks, 2: 243–257, 1989.
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.
G A Carpenter and S Grossberg. Pattern Recognition by Self-organizing Neural Networks. MIT Press, Cambridge, Ma, 1991.
V Cerny. Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45: 41–51, 1985.
C H Chen, editor. Pattern Recognition and Artificial Intelligence. Academic Press, New York, 1976.
N Chomsky: Syntactic Structures. Mouton, Hague, 6th edition, 1966.
N Chomsky, J P B Allen, and P Van Buren: Chomsky: Selected Readings. Oxford University Press, London-New York, 1971.
W F Clocksin, and C S Mellish: Programming in Prolog. Springer Verlag, Berlin-New-York-Tokyo, 1981.
Dasarathy 91] B V Dasarathy: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Comp. Society Press, Los Alamitos, Ca, 1991.
J E Dayhoff: Neural Network Architectures: An Introduction. Van Nostrand Reinhold, New York, 1990.
P A Devijver, and J Kittler: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ, 1982.
P A Devijver, and J Kittler: Pattern Recognition Theory and Applications. Springer Verlag, Berlin-New York-Tokyo, 1986.
R O Duda, and P E Hart: Pattern Classification and Scene Analysis. John Wiley and Sons, New York, 1973.
R C Eberhart, and R W Dobbins: Neural Network PC Tools: A Practical Guide. Academic Press, San Diego, Ca, 1990.
S Even. Graph Algorithms. Computer Science Press, Rockville, Md, 1979.
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.
Foley 72] D H Foley. Consideration of sample and feature size. IEEE Transactions on Information Theory, IT-18(5):618–626, 1972.
K S Fu: Sequential Methods in Pattern Recognition and Machine Learning. Academic Press, New York, 1968.
K S Fu: Syntactic Methods in Pattern Recognition. Academic Press, New York, 1974.
K S Fu: Syntactic Pattern Recognition — Applications. Springer Verlag, Berlin, 1977.
K S Fu: Picture syntax. In S K Chang and K S Fu, editors, Pictorial Information Systems, pages 104–127. Springer Verlag, Berlin, 1980.
K S Fu: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs, NJ, 1982.
Fukunaga 90] K Fukunaga: Introduction to Statistical Pattern Recognition. Academic Press, Boston, 2nd edition, 1990.
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.
D E Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Ma, 1989.
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.
S Grossberg: Neural pattern discrimination. Journal of Theoretical Biology, 27: 291–337, 1970.
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.
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.
F Harary: Graph Theory. Addison-Wesley, Reading, Ma, 1969.
P J Hayes: In defense of logic. In Proceedings of 5th IJCAI, Cambridge, Ma, 1977.
R Hecht-Nielsen: Neurocomputing. Addison-Wesley, Reading, Ma, 1990.
J J Hopfield, and D W Tank: Neural computation of decisions in optimization problems. Biological Cybernetics, 52: 141–152, 1985.
J J Hopfield, and D W Tank: Computing with neural circuits: A model. Science, 233: 625–633, 1986.
Johnson and Wichern 90] R A Johnson, and D W Wichern: Applied Multivariate Statistical Analysis. Prentice-Hall, Englewood Cliffs, NJ, 2nd edition, 1990.
J S Judd. Neural Network Design and the Complexity of Learning. MIT Press, Cambridge, Ma, 1990.
L Kaufman, and P J Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York, 1990.
S Kirkpatrick, C D Gelatt, and M P Vecchi: Optimization by simulated annealing. Science, 220: 671–680, 1983.
Kohonen 89] T Kohonen: Self-Organization and Associative Memory. Springer Verlag, Berlin-New York-Tokyo, 3rd edition, 1989.
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.
R Kowalski: Logic for Problem Solving. North Holland, Amsterdam, 1979.
H T Lau: Algorithms on Graphs. TAB Professional and Reference Books, Blue Ridge Summit, Pa, 1989.
J MacQueen: Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium — 1, pages 281–297, 1967.
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.
J A McHugh: Algorithmic Graph Theory. Prentice-Hall, Englewood Cliffs, NJ, 1990.
L L McQuitty: Pattern-Analytic Clustering: Theory, Method, Research, and Configural Findings. University Press of America, Lanham, NY, 1987.
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.
R S Michalski, J G Carbonell, and T M Mitchell: Machine Learning I, II. Morgan Kaufmann Publishers, Los Altos, Ca, 1983.
Minsky 88] M L Minsky: Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, Ma, 2nd edition, 1988.
R H Mohring, editor. Graph-Theoretic Concepts in Computer Science - 16th WG’90, Berlin-New York-Tokyo, 1991. Springer Verlag.
M C Mozer: The Perception of Multiple Objects: A Connectionist Approach. MIT Press, Cambridge, Ma, 1991.
M Nagl, editor: Graph-Theoretic Concepts in Computer Science - 15th WG ‘89, Berlin-New York-Tokyo, 1990. Springer Verlag.
Niemann 90] H Niemann: Pattern Analysis and Understanding. Springer Verlag, Berlin-New York-Tokyo, 2nd edition, 1990.
N J Nilsson: Problem Solving Methods in Artificial Intelligence. McGraw Hill, New York, 1971.
N J Nilsson: Principles of Artificial Intelligence. Springer Verlag, Berlin, 1982.
E Oja. Subspace Methods of Pattern Recognition. Research Studies Press, Letchworth, England, 1983.
R H J M Otten, and L P P P van Ginneken: The Annealing Algorithm. Kluwer Academic Publishers, Norwell, Ma, 1989.
E A Patrick, and J M Fattu: Artificial Intelligence with Statistical Pattern Recognition. Prentice-Hall, Englewood Cliffs, NJ, 1986.
M Pavel. Fundamentals of Pattern Recognition. M. Dekker, New York, 1989.
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.
H Pospesel: Predicate Logic. Prentice-Hall, Englewood Cliffs, NJ, 1976.
C R Rao: Linear Statistical Inference and its Application. John Wiley and Sons, New York, 1965.
G J E Rawlins: Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo, Ca, 1991.
H Reichgelt: Knowledge Representation: An AI Perspective. Ablex Publishing Corporation, Norwood, NJ, 1991.
S K Rogers, and M Kabrisky: An Introduction to Biological and Artificial Neural Networks for Pattern Recognition. SPIE, Bellingham, Wa, 1991.
H C Romesburg. Cluster Analysis for Researchers. Lifetime Learning Publications, Belmont, Ca, 1984.
R Rosenblatt: Principles of Neurodynamics. Spartan books, Washington, D.C., 1962.
A Rosenfeld: Picture Languages — Formal Models for Picture Recognition. Academic Press, New York, 1979.
D Rumelhart, and J McClelland: Parallel Distributed Processing. MIT Press, Cambridge, Ma, 1986.
D Schutzer: Artificial Intelligence, An Application-Oriented Approach. Van Nostrand Reinhold, New York, 1987.
Sedgewick 84] R Sedgewick: Algorithms. Addison-Wesley, Reading, Ma, 2nd edition, 1984.
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.
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.
G L Simons. Introducing Artificial Intelligence. NCC Publications, Manchester, 1984.
Simpson 90] P K Simpson: Artificial Neural Systems: Foundations
Paradigms, Applications, and Implementations. Pergamon Press, New York, 1990.
J Sklansky: Pattern Classifiers and Trainable Machines. Springer Verlag, New York, 1981.
M Sonka: A new texture recognition method. Computers and Artificial Intelligence, 5 (4): 357–364, 1986.
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.
J R Ullmann: An algorithm for subgraph isomorphism. Journal of the Association for Computing Machinery, 23 (1): 31–42, 1976.
P J M van Laarhoven: Theoretical and Computational Aspects of Simulated Annealing. Centrum voor Wiskunde en Informatik, Amsterdam, 1988.
P J M van Laarhoven, and E H L Aarts: Simulated Annealing: Theory and Applications. Dordrecht and Kluwer Academic Publisher, Norwell, Ma, 1987.
P D Wasserman: Neural Computing — Theory and Practice. Van Nostrand Rheinhold, New York, 1989.
H Wechsler: Computational Vision. Academic Press, London — San Diego, 1990.
P H Winston, editor. The Psychology of Computer Vision. McGraw Hill, New York, 1975.
Winston 84] P H Winston: Artificial Intelligence. Addison-Wesley, Reading, Ma, 2nd edition, 1984.
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.
T Y Young, and T W Calvert: Classification, Estimation, and Pattern Recognition. American Elsevier, New YorkLondon-Amsterdam, 1974.
Z Zdrahal: A structural method of scene analysis. In Proceedings of IJCA I-81, pages 680–682, Vancouver, BC, Canada, 1981.
M Zeidenberg: Neural Network Models in Artificial Intelligence. E. Hoerwood, New York, 1990.
Y T Zhou: Artificial Neural Networks for Computer Vision. Springer Verlag, New York, 1992.
H J Zimmermann, L A Zadeh, and B R Gaines: Fuzzy Sets and Decision Analysis. North Holland, Amsterdam-New York, 1984.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-1-4899-3216-7_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-412-45570-4
Online ISBN: 978-1-4899-3216-7
eBook Packages: Springer Book Archive