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Pattern recognition with an adaptive joint transform correlator.

Victor H Diaz-Ramirez1, Vitaly Kober, Josue Alvarez-Borrego

  • 1Centro de Investigación Cientifica y de Educación Superior de Ensenada, Division de Fisica Aplicada, Mexico. vhdiaz@cicese.mx

Applied Optics
|August 24, 2006
PubMed
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This study introduces an adaptive joint transform correlator for real-time pattern recognition. It utilizes a novel iterative algorithm based on synthetic discriminant functions for robust target discrimination in cluttered environments.

Area of Science:

  • Optoelectronics
  • Pattern Recognition
  • Image Processing

Background:

  • Real-time pattern recognition is crucial for various applications.
  • Existing methods struggle with cluttered backgrounds and false objects.
  • Adaptive correlators offer potential for improved discrimination.

Purpose of the Study:

  • To present an adaptive joint transform correlator (AJTC) for enhanced real-time pattern recognition.
  • To develop a novel iterative algorithm for generating a robust reference image.
  • To investigate optodigital implementation methods and validate performance.

Main Methods:

  • Developed a new iterative algorithm based on synthetic discriminant functions (SDFs) to create an adaptive reference image.
  • Incorporated calibration lookup tables for optoelectronic elements into the AJTC design.

Related Experiment Videos

  • Considered two optodigital implementation strategies for the proposed AJTC.

    • Main Results:

    • The generated reference image effectively discriminates targets from false objects and clutter.
    • Experimental results demonstrate the AJTC's capability in real-time pattern recognition.
    • Performance was validated through comparison with computer simulations.

    Conclusions:

    • The proposed adaptive joint transform correlator offers a reliable solution for real-time pattern recognition.
    • The novel SDF-based training algorithm enhances discrimination capabilities in complex scenes.
    • Optodigital implementation is feasible and yields performance comparable to simulations.