Soft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition. The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to provide a strong foundation for the reader. The supervised approach to pattern classification and model-based approach to occluded object recognition are treated in one framework , one based on either a conventional interpretation or a new interpretation of multidimensional fuzzy implication (MFI) and a novel notion of fuzzy pattern vector (FPV). By combining practice and theory, a completely independent design methodology was developed in conjunction with this supervised approach on a unified framework, and then tested thoroughly against both synthetic and real-life data. In the field of soft computing, such an application-oriented design study is unique in nature. The monograph essentially mimics the cognitive process of human decision making, and carries a message of perceptual integrity in representational diversity. Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision. Other academics and practitioners will also find the book valuable.

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“This book addresses the use of soft computing techniques for pattern recognition problems, starting with an interesting overview of how the conventional approach to pattern recognition differs from the soft computing approach. ... The book achieves a well-balanced treatment of theory, mathematics, and applications. Each chapter has a good number of references, and ... each includes interesting experimental results. ... this book would be useful for research scholars and scientists working in the field of pattern recognition.” (S. Ramakrishnan, Computing Reviews, March, 2013)

“The book presents the research work of the author regarding soft computing methods, such as fuzzy relational calculus, genetic algorithms and neural networks, for the tasks of pattern classification and occluded object recognition. ... The entire methodology is thoroughly tested on real-world applications and addresses researchers in pattern classification and computer vision.” (Catalin Stoean, Zentralblatt MATH, Vol. 1260, 2013)

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