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

Sensors

Leonardo M. Honório¹  Ivo C. S. Junior¹  Maria Júlia R. Aguiar¹  Tiago da Rocha Alves¹  Vinícius F. Vidal¹ 

[1] Department of Electrical Engineering, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil;

关键词: metaheuristics;    bundle adjustment;    panorama image;    bat algorithm;    grey wolf optimizer;    arithmetic optimization algorithm;   

DOI  :  10.3390/s21155054

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown