Panorama Stitching Based on SIFT Algorithm and Levenberg-Marquardt Optimization

Zhong, Min; Zeng, Jiguo; Xie, Xiao-Liang

doi:10.1016/j.phpro.2012.05.139

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…The numbers of extracted features depend on the nature of the images and the used parameters. So interest points become a popular tool for detecting objects, creating 3D models, and creating panoramas [1]. The creation of panoramas is one of the most popular applications for feature extraction because under normal conditions we need the images to be in order and have the same viewpoint and scale.…”

Section: Related Workmentioning

confidence: 99%

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Elorabi,

Zekry,

Mohamed

2023

J. Phys.: Conf. Ser.

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Image registration has been increasingly employed in various applications such as target identification, 3D mapping, and motion tracking. The main idea of Image registration is aligning two or more images of the same scene captured from different viewpoints, at different times. Scale-invariant feature transform, SIFT, is considered one of the most robust algorithms used in image registration for extracting and matching features under different conditions. Using SIFT algorithm default parameters in Matching UAV and satellite Images provides unreliable results due to the nature of aerial images because the dynamic range is quite low. The number of extracted features depends on the image content and the selected parameters. In this paper we tuned SIFT parameters to get the best performance with aerial images, to increase the number of features (SM) and the correct match rate (CMR) which increases the efficiency of the process of registration. The algorithm is validated by matching a large number of aerial images taken by mini-UAV with satellite images for the same region.

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Section: Related Workmentioning

confidence: 99%

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Elorabi,

Zekry,

Mohamed

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…For the five metaheuristic algorithms focused in this article, the time complexity is dependant on the number of individuals/agents, dimension of the problem and maximum number of iterations. Overall, by analyzing the steps of each algorithm, the computational complexity t is stated in Equation (27).…”

Section: Time Complexity Analysis For the Metaheuristics Algorithmsmentioning

confidence: 99%

“…The research on images alignment for stitching culminated, to some extent, in the use of bundle adjustment [ 19 ] to simultaneously optimize the relative positions of the images. Several types and classes of optimization-based algorithms [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ] can be used to align all images to a common frame of reference typically, the classic Levenberg–Marquardt method is used to optimize the intrinsic and extrinsic parameters of the camera [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

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

Aguiar

Alves

Honório

et al. 2021

Sensors

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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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Review on image-stitching techniques

Wang

Yang

2020

Multimedia Systems

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Panorama Stitching Based on SIFT Algorithm and Levenberg-Marquardt Optimization

Cited by 8 publications

References 3 publications

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Optimizing SIFT algorithm parameters for better matching UAV and satellite images

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

Review on image-stitching techniques

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