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Fuzzy tree automata and syntactic pattern recognition.

¹Department of Mathematical Sciences, Memphis State University, Memphis, TN 38152.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces fuzzy tree automata for pattern recognition, enabling fuzzy tree representations to form a Boolean algebra. This approach enhances processing of complex patterns like houses and churches.

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Area of Science:

Computer Science
Artificial Intelligence
Pattern Recognition

Background:

Syntactic pattern recognition traditionally uses tree representations.
Fuzzy logic offers a framework for handling uncertainty in pattern data.
Existing methods may lack robustness in processing imprecise patterns.

Purpose of the Study:

To define and investigate fuzzy tree automata for pattern processing.
To establish the properties of fuzzy root-to-frontier recognizable trees.
To apply fuzzy tree automata to syntactic pattern recognition with quantitative measures.

Main Methods:

Formal definition and investigation of fuzzy tree automata.
Demonstration of closure properties (intersection, union, complementation) for fuzzy trees.
Development of quantitative measures for approximate shapes (e.g., triangles, rectangles, crosses).

Main Results:

The class of fuzzy root-to-frontier recognizable trees forms a Boolean algebra.
Fuzzy tree automata successfully process fuzzy tree representations of patterns.
Quantitative measures are defined and applied to examples like houses and churches.

Conclusions:

Fuzzy tree automata provide a robust framework for syntactic pattern recognition with uncertainty.
The Boolean algebra property enhances the theoretical foundation for fuzzy tree processing.
Applications are foreseen in pattern recognition, image processing, and artificial intelligence.