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Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image

Maria Júlia R Aguiar1, Tiago da Rocha Alves1, Leonardo M Honório1

  • 1Department of Electrical Engineering, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil.

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Summary
This summary is machine-generated.

This study enhances image stitching for industrial applications by comparing metaheuristics like the Bat Algorithm against traditional methods. Bio-inspired algorithms significantly improve panorama reconstruction quality over the Levenberg-Marquardt approach.

Keywords:
Salp Swarm Algorithmarithmetic optimization algorithmbat algorithmbundle adjustmentgrey wolf optimizermetaheuristicspanorama imageparticle swarm optimization

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Image stitching is crucial for creating panoramas from multiple images, with applications in remote sensing and industrial inspection.
  • Traditional methods, like the Levenberg-Marquardt algorithm, have limitations for industrial-grade applications due to reconstruction flaws.
  • Precise image positioning using RGBD robots is explored to enhance stitching quality.

Purpose of the Study:

  • To evaluate the effectiveness of bio-inspired algorithms for optimizing image stitching.
  • To compare the performance of metaheuristics against the classical Levenberg-Marquardt method in panorama construction.
  • To determine if metaheuristics offer superior solutions for industrial-grade image stitching.

Main Methods:

  • Utilizing an RGBD robot for precise image acquisition and positioning.
  • Implementing and comparing several bio-inspired algorithms: Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm, and Particle Swarm Optimization.
  • Assessing the efficiency and competitiveness of these metaheuristics against the Levenberg-Marquardt numerical method.

Main Results:

  • Metaheuristic algorithms demonstrated superior performance in image stitching compared to the traditional Levenberg-Marquardt method.
  • The proposed bio-inspired approaches yielded better solutions for panorama reconstruction.
  • The study confirmed the competitiveness of metaheuristics for high-quality image stitching.

Conclusions:

  • Bio-inspired algorithms offer a more effective approach to image stitching for industrial applications.
  • Metaheuristics provide a viable and competitive alternative to traditional methods for panorama construction.
  • The use of precise robotic positioning combined with metaheuristics significantly improves stitching quality.