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An evolutionary learning system for synthesizing complex morphological filters.

M A Zmuda1, L A Tamburino, M M Rizki

  • 1Spectra Res., Centerville, OH.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1996
PubMed
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This study introduces MORPH, an evolutionary learning system that semi-automates the creation of morphological programs. MORPH enhances program diversity and performance through iterative synthesis, combination, and selection for improved target recognition.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automated generation of morphological programs is crucial for complex pattern recognition tasks.
  • Existing methods may lack efficiency or adaptability in feature extraction.

Purpose of the Study:

  • To present MORPH, a novel system for semi-automating the generation of morphological programs.
  • To enhance the diversity and performance of morphological programs through evolutionary learning.

Main Methods:

  • Utilizes an evolutionary learning approach with a population of morphological programs.
  • Employs a two-phase learning cycle: feature synthesis for diversity and program combination for enhanced performance.
  • Incorporates stochastic selection to refine the program population.

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Main Results:

  • Demonstrates successful application in both binary and grayscale target recognition problems.
  • The system effectively increases population diversity and combines programs for superior performance compared to individual components.

Conclusions:

  • MORPH offers a viable semi-automated approach to generating effective morphological programs.
  • The evolutionary strategy enhances feature extraction and program optimization for recognition tasks.