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Evolutionary Image Registration: A Review.

Cătălina-Lucia Cocianu1, Cristian Răzvan Uscatu1, Alexandru Daniel Stan1

  • 1Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania.

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Summary

Nature-inspired algorithms offer a robust alternative for image registration, a key process in image analysis. This review summarizes evolutionary methods, comparing their components, fitness functions, and accuracy for improved image alignment.

Keywords:
accuracy indexevolutionary algorithmsfitness functionsimage registrationimage similarity indicators

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image registration is crucial for various analysis tasks like recognition and classification.
  • It addresses real-world problems in fields such as remote sensing, medical imaging, and surveillance.
  • Nature-inspired algorithms and metaheuristics are emerging as effective alternatives to traditional optimization methods for image registration.

Purpose of the Study:

  • To investigate and summarize state-of-the-art evolutionary-based image registration methods.
  • To provide a comprehensive review of algorithms selected using the PRISMA 2020 methodology.
  • To compare these methods based on key components and performance metrics.

Main Methods:

  • Systematic literature review using the PRISMA 2020 guidelines.
  • Analysis and comparison of evolutionary algorithms applied to image registration.
  • Evaluation of fitness functions, image similarity measures, and accuracy indexes.

Main Results:

  • Evolutionary-based methods show significant promise as alternatives to direct optimization techniques.
  • A detailed comparison of different evolutionary components and their impact on registration accuracy is presented.
  • Key metrics for evaluating algorithm performance in image alignment are identified.

Conclusions:

  • Evolutionary algorithms represent a powerful approach to image registration.
  • Further research can build upon this review to develop more efficient and accurate registration techniques.
  • The findings offer valuable insights for researchers and practitioners in image processing and computer vision.