Abstract
The basic aim of this research monograph is to develop a unified approach to supervised pattern classification (Tou and Gonzalez, Pattern Recognition Principles. Addison-Wesley, Reading, 1974) and model based occluded object recognition (Koch and Kashyap, IEEE Trans Pattern Anal Machine lntell. 9(4):483–494, 1987; Ray and Dutta Mazumder, Pattern Recogn Lett 9:351–360, 1989). To perform this task we essentially consider soft computing tools, viz., fuzzy relational calculus (FRC) (Pedrycz, Fuzzy Sets Syst 16:163–174, 1985, Pattern Recogn 23(1/2):121–146, 1990), genetic algorithm (GA) (Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, 1989; Michalewicz, Genetic Algorithm + Data Structures = Evolution Programs, Springer, New York, 1994) and multilayer perceptron (MLP) (Pao, Adaptive pattern recognition and neural networks. Addison Wesley, Reading, 1989). The supervised approach to pattern classification and model based approach to occluded object recognition are treated in one framework which is based on either conventional interpretation or new interpretation of multidimensional fuzzy implication (MFI) (Sugeno and Takagi, Fuzzy Sets Syst 9:313–325, 1983; Tsukamoto, Advance in Fuzzy Set Theory and Applications. North-Holland, Amsterdam, 137–149, 1979) and a novel notion of fuzzy pattern vector (FPV). In the context of representation of knowledge about patterns and/or objects we try to generalize the concept of feature vector by fuzzy feature vector. Readers are advised to read Appendix-A before they go into the details of classification (recognition) concept based on soft computing tools.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley, Reading, Bonston, 1989)
M. W. Koch, R. L. Kashyap, Using polygons to recognize and locate partially occluded objects. IEEE Trans. Pattern Anal. Mach. lntell. 9(4), 483–494 (1987)
Z. Michalewicz, Genetic Algorithm + Data Structures = Evolution Programs (Springer, New York, 1994)
M. Mizumoto, Extended Fuzzy Reasoning, in Approximate Reasoning in Expert Systems, ed. by M. M. Gupta, A. Kandel, W. Bandler, J. B. Kiszka (North-Holland, Amsterdam, 1985), pp. 71–85
A. Newell, H.A. Simon, Human problem solving (Prentice-Hall, Englewood Cliffs, 1972)
Y. H. Pao, Adaptive Pattern Recognition and Neural Networks (Addison Wesley Publishing Company, Reading, Boston, 1989)
W. Pedrycz, Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data. Fuzzy Sets Syst. 16, 163–174 (1985)
W. Pedrycz, Fuzzy sets in pattern recognition methodology and methods. Pattern Recogn. 23(1/2), 121–146 (1990)
K. S. Ray, D. Dutta Mazumder, Application of differential geometry to recognize and locate partially occluded objects. Pattern Recogn. Lett. 9, 351–360 (1989)
G. Schulz, Fuzzy Rule Based Expert Systems and Genetic Machine Learning (Physica-verlag, Germany, 1995)
M. Sugeno, T. Takagi, Multidimensional fuzzy reasoning. Fuzzy Sets Syst. 9, 313–325 (1983)
J. T. Tou, R. C. Gonzalez, Pattern Recognition Principles (Addison-Wesley, Reading, 1974)
Y. Tsukamoto, An approach to Fuzzy Reasoning Method, in Advance in Fuzzy Set Theory and Applications, ed. by M. M. Gupta, R. K. Ragade, R. R. Yager (North-Holland, Amsterdam, 1979), pp. 137–149
L. A. Zadeh, Theory of Approximate Reasoning, in Machine Intelligence, ed. by Hayes J. E., Donald Michie, L. I. Mikulich (Ellis Horwood, Chichester, New York, 1970), pp. 149–194
L. A. Zadeh, Fuzzy Sets and their Applications to Classification and Clustering, in Classification and Clustering, ed. by J. Van Ryzin (Academic, New York, 1977), pp. 251–299
L. A. Zadeh, K. S. Fu, K. Tanaka, M. Shimura (eds.), Fuzzy Sets and their Applications to Cognitive and Decision Processes (Academic, New York, 1975)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media New York
About this chapter
Cite this chapter
Ray, K.S. (2012). Soft Computing Approach to Pattern Classification and Object Recognition. In: Soft Computing Approach to Pattern Classification and Object Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5348-2_1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-5348-2_1
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5347-5
Online ISBN: 978-1-4614-5348-2
eBook Packages: Computer ScienceComputer Science (R0)