Underwater image stitching using globally optimal local homographies with application to seafloor mosaicing

Elnashef, Bashar; Filin, Sagi

doi:10.23919/oceans44145.2021.9705973

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Furthermore, an efficient feature-based image mosaicking (FIM) method has utilized multiple underwater robots for the topology estimation process, delivering an accurate map [ 74 ]. Finally, a recent underwater image blending algorithm based on globally optimal local homographies has been applied to seafloor mosaicking [ 75 ]. It was shown that the adoption of a local warp model improved the alignment and minimized local distortions compared to global approaches.…”

Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

“…It was shown that the adoption of a local warp model improved the alignment and minimized local distortions compared to global approaches. The authors also proposed a three-step seafloor mosaicking pipeline consisting of (a) image keypoint extraction and matching, (b) camera orientation estimation, and (c) the fusion of both previous stages, providing a natural-looking mosaic [ 75 ].…”

Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

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Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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show abstract

“…Some authors advanced in the image matching and stitching algorithms, a very important part to estimate the camera motion reliably, which in turn has a direct influence in the correct location of each image in the mosaic frame. For instance, 1) Elnashef et al Elnashef and Filin (2021) improved the image aligning minimizing also the local distortion, with testing datasets recorded from an AUV in rectilinear transects, 2) Abaspur,et al Kazerouni et al (2020) applied (2D) 2 PCA Zhang and Zhou (2005) and A-KAZE Fernández Alcantarilla ( 2013) key-points extraction to match images of underwater pipes enhanced with a noise removal procedure based on the Fast Fourier Transform, and destined to the construction of photo-mosaics, and 3) Garcia-Fidalgo et al Garcia-Fidalgo et al (2016) reduced the image matching time characterizing them with bags of binary words; however, the process required a complete cross-wise comparison of each image with all the rest.…”

Section: Introductionmentioning

confidence: 99%

Image Filtering to Increase the Efficiency in the Construction of Underwater Photomosaics

Castelló,

Bonin-Font,

Codina

2024

Preprint

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This paper describes an innovative methodology designed to filter out images with redundant information, initially selected to form photo-mosaics of the seabed. Collecting visual data of the sea bottom using underwater robots usually entails hundreds or even thousands of images if missions are performed in large areas and/or have long durations. All these images usually present multiple overlapping parts that need to be filtered out wisely in order to avoid useless repetitive information, questionable pixel aggregations, unreal colour blends, or the collapse of the computational resources dedicated to the mosaic building process. Experiments have been done with datasets grabbed from an Autonomous Underwater Vehicle while observing marine habitats of special ecological interest. Results show important reductions of visual data destined to the mosaics without significant loss of relevant information to be included in them, saving, drastically, time and data processing efforts.

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Acoustic Positioning Enhanced Optical Image Mosaics: A Collaborative Robot-Assisted Approach

Azam,

Kien,

Loke

et al. 2024

OCEANS 2024 - Singapore

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Underwater image stitching using globally optimal local homographies with application to seafloor mosaicing

Cited by 3 publications

References 25 publications

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Image Filtering to Increase the Efficiency in the Construction of Underwater Photomosaics

Acoustic Positioning Enhanced Optical Image Mosaics: A Collaborative Robot-Assisted Approach

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