Journal
SENSORS
Volume 21, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/s21155054
Keywords
metaheuristics; bundle adjustment; panorama image; bat algorithm; grey wolf optimizer; arithmetic optimization algorithm; Salp Swarm Algorithm; particle swarm optimization
Funding
- TBE and EDP over supervision of ANEEL [PD-02651-0013/2017]
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This paper explores the use of an RGBD robot and bio-inspired algorithms to optimize the quality of image stitching, with results showing that metaheuristics are more effective than traditional approaches.
The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg-Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg-Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.
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