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Structural pattern recognition using genetic algorithms with specialized operators.

K G Khoo1, P N Suganthan

  • 1Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces an improved genetic algorithm (GA) for structural pattern recognition. The optimized GA enhances attributed relational graph (ARG) matching for faster, more accurate object recognition and pose estimation.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Model-based recognition systems often use attributed relational graph (ARG) matching.
  • Standard genetic algorithms (GA) can be slow and suboptimal for ARG matching.

Purpose of the Study:

  • To enhance GA-based ARG matching for faster convergence and improved mapping accuracy.
  • To enable recognition of multiple object instances and determine their pose.

Main Methods:

  • Developed a GA optimization procedure for ARG matching.
  • Represented solutions as integer strings mapping scene to model vertices.
  • Utilized specialized crossover and mutation operators for ARG matching.
  • Incorporated a pose-clustering algorithm for error elimination and pose determination.

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Main Results:

  • The proposed GA algorithm demonstrated faster convergence than the standard genetic algorithm (SGA).
  • Achieved higher quality mappings between scene and model ARGs.
  • Successfully recognized multiple instances of model objects.
  • The pose-clustering algorithm effectively eliminated incorrect mappings.

Conclusions:

  • The specialized GA significantly improves ARG matching efficiency and accuracy.
  • The method is effective for robust structural pattern recognition and pose estimation.
  • This approach offers superior performance in complex recognition tasks.