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  • 1
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    Optical and hydroacoustical measurements of bubble-flow seepage offshore Oregon (~46° N) during RV FALKOR cruise FK190612 (2022)
    PANGAEA
    Details
    Publication Date: 2023-03-01
    Description: Two lander-based devices, the Bubble-Box and GasQuant-II, were used to investigate the spatial and temporal variability and total gas flow rates of a seep area offshore Oregon, United States. The Bubble-Box is a stereo camera–equipped lander that records bubbles inside a rising corridor with 80 Hz, allowing for automated image analyses of bubble size distributions and rising speeds. GasQuant is a hydroacoustic lander using a horizontally oriented multibeam swath (Imagenex DeltaT) to record the backscatter intensity of bubble streams passing the swath plain. The experimental set up at the Astoria Canyon site at a water depth of about 500m aimed at calibrating the hydroacoustic GasQuant data with the visual Bubble-Box data for a spatial and temporal flow rate quantification of the site. For about 90h in total, both systems were deployed simultaneously and pressure and temperature data were recorded using a CTD as well. The data presented here contain post-processed information collected with the two lander-based devices during the cruise FK190612 on board RV FALKOR from the Schmidt-Ocean Institute in June 2019 (https://schmidtocean.org/cruise/methane-seeps-at-edge-of-hydrate-stability/). The first dataset contains post-processed ASCII information of the optic data recorded with the Bubble-Box system during two deployments (BBM-11 and BBM-17) and includes overall results of bubble sizes, volumes, rising speeds and flow rates of the observed bubble streams. A second dataset contains the acoustic backscatter of a seep site collected with the GasQuant II system during two deployments (GQM-3 and GQM-4). Each file contains the uncalibrated backscatter averaged in time (1 minute average) of the original dataset recorded with the GasQuant II. Files are provided in the Imagenex DeltaT data output format (*.83b) and can be easily visualized and inspected using FMMidwater (QPS) .
    Keywords: Astoria canyon; Astoria Canyon, offshore Oregon; Binary Object; Binary Object (File Size); BMB; BubbleBox; Bubble monitoring box; Bubbles; Event label; Falkor; File content; FK190612; FK190612_BBM-11; FK190612_BBM-17; FK190612_GQM-3; FK190612_GQM-4; GASQUANT; Gas Quantification, Lander; GasQuant-II; GEOMAR; Helmholtz Centre for Ocean Research Kiel; Hydroacoustic quantification; Methane Seeps; optical bubble measurements
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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    DATA PANGAEA
  • 2
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    An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-06-01
    Description: The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system.
    Electronic ISSN: 2220-9964
    Topics: Architecture, Civil Engineering, Surveying , Geosciences
    Published by Molecular Diversity Preservation International
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  • 3
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    Deep Sea Robotic Imaging Simulator (2021)
    Springer
    In:  In: Pattern Recognition. ICPR International Workshops and Challenges. , ed. by Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G. M., Mei, T., Bertini, M., Escalante, H. J. and Vezzani, R. Springer, Cham, pp. 375-389.
    Details
    Publication Date: 2021-08-03
    Description: Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator).
    Type: Book chapter , NonPeerReviewed
    Format: text
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  • 4
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    Considering Spherical Refraction in Visual Ocean Gas Release Quantification (2020)
    IEEE
    In:  [Paper] In: 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020, 10.07.2020, Chongqing; China . Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning ; pp. 64-69 .
    Details
    Publication Date: 2021-01-11
    Description: Compared to traditional gas flow quantification methods, the stereo vision system has some advantages. However, underwater vision systems usually suffer from light refraction which can degrade the measurement accuracy from images. Cameras centered in spherical glass housings, dome ports, can theoretically avoid refraction, but misalignments in the dome create even more complex refraction effects than cameras behind flat glass windows. This paper introduces the spherical refraction model into a stereo vision gas flow quantification system. Also, this paper adds some contributions to an existing bubble quantification workflow for bubble size histogram and bubble volume estimation. First, the spherical glass dome port and the light propagation are modeled, and then the camera system is calibrated via underwater/in-air image pairs. Afterwards, the Epipolar Geometry Constraint is used to optimize the bubble matching. For volume estimation, an ellipsoid triangulation method is employed to improve ellipsoidal volume estimation. According to the calibration experiments and control experiments, the results show that the stereo vision gas flow quantification system can produce the volume of gas release accurately, which satisfies the requirements of long-term gas release monitoring in marine science.
    Type: Conference or Workshop Item , NonPeerReviewed
    Format: text
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  • 5
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    Adjustment and Calibration of Dome Port Camera Systems for Underwater Vision (2019)
    Springer
    In:  In: Pattern Recognition: 41st DAGM German Conference, DAGM GCPR 2019, Dortmund, Germany, September 10–13, 2019, Proceedings. , ed. by Fink, G. A., Frintrop, S. and Jiang, X. Lecture Notes in Computer Science, 11824 . Springer, Cham, pp. 79-92. ISBN 978-3-030-33676-9
    Details
    Publication Date: 2020-02-26
    Description: Dome ports act as spherical windows in underwater housings through which a camera can observe objects in the water. As compared to flat glass interfaces, they do not limit the field of view, and they do not cause refraction of light observed by a pinhole camera positioned exactly in the center of the dome. Mechanically adjusting a real lens to this position is a challenging task, in particular for those integrated in deep sea housings. In this contribution a mechanical adjustment procedure based on straight line observations above and below water is proposed that allows for accurate alignments. Additionally, we show a chessboard-based method employing an underwater/above-water image pair to estimate potentially remaining offsets from the dome center to allow refraction correction in photogrammetric applications. Besides providing intuition about the severity of refraction in certain settings, we demonstrate the methods on real data for acrylic and glass domes in the water.
    Type: Book chapter , NonPeerReviewed , info:eu-repo/semantics/bookPart
    Format: text
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  • 6
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    In-Situ Joint Light and Medium Estimation for Underwater Color Restoration (2021)
    In:  [Paper] In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 11.-17.10.2021, Montreal, Canada .
    Details
    Publication Date: 2022-01-14
    Type: Conference or Workshop Item , NonPeerReviewed , info:eu-repo/semantics/conferenceObject
    Format: text
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  • 7
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    Virtually throwing benchmarks into the ocean for deep sea photogrammetry and image processing evaluation (2022)
    Copernicus Publications (EGU)
    Details
    Publication Date: 2024-02-07
    Description: Vision in the deep sea is acquiring increasing interest from many fields as the deep seafloor represents the largest surface portion onEarth. Unlike common shallow underwater imaging, deep sea imaging requires artificial lighting to illuminate the scene in perpetualdarkness. Deep sea images suffer from degradation caused by scattering, attenuation and effects of artificial light sources and havea very different appearance to images in shallow water or on land. This impairs transferring current vision methods to deep seaapplications. Development of adequate algorithms requires some data with ground truth in order to evaluate the methods. However,it is practically impossible to capture a deep sea scene also without water or artificial lighting effects. This situation impairs progressin deep sea vision research, where already synthesized images with ground truth could be a good solution. Most current methodseither render a virtual 3D model, or use atmospheric image formation models to convert real world scenes to appear as in shallowwater appearance illuminated by sunlight. Currently, there is a lack of image datasets dedicated to deep sea vision evaluation. Thispaper introduces a pipeline to synthesize deep sea images using existing real world RGB-D benchmarks, and exemplarily generatesthe deep sea twin datasets for the well known Middlebury stereo benchmarks. They can be used both for testing underwater stereomatching methods and for training and evaluating underwater image processing algorithms. This work aims towards establishingan image benchmark, which is intended particularly for deep sea vision developments.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 8
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    Quantitatively Monitoring Bubble-Flow at a Seep Site Offshore Oregon: Field Trials and Methodological Advances for Parallel Optical and Hydroacoustical Measurements (2022)
    Frontiers
    Details
    Publication Date: 2024-02-07
    Description: Two lander-based devices, the Bubble-Box and GasQuant-II, were used to investigate the spatial and temporal variability and total gas flow rates of a seep area offshore Oregon, United States. The Bubble-Box is a stereo camera–equipped lander that records bubbles inside a rising corridor with 80 Hz, allowing for automated image analyses of bubble size distributions and rising speeds. GasQuant is a hydroacoustic lander using a horizontally oriented multibeam swath to record the backscatter intensity of bubble streams passing the swath plain. The experimental set up at the Astoria Canyon site at a water depth of about 500 m aimed at calibrating the hydroacoustic GasQuant data with the visual Bubble-Box data for a spatial and temporal flow rate quantification of the site. For about 90 h in total, both systems were deployed simultaneously and pressure and temperature data were recorded using a CTD as well. Detailed image analyses show a Gaussian-like bubble size distribution of bubbles with a radius of 0.6–6 mm (mean 2.5 mm, std. dev. 0.25 mm); this is very similar to other measurements reported in the literature. Rising speeds ranged from 15 to 37 cm/s between 1- and 5-mm bubble sizes and are thus, in parts, slightly faster than reported elsewhere. Bubble sizes and calculated flow rates are rather constant over time at the two monitored bubble streams. Flow rates of these individual bubble streams are in the range of 544–1,278 mm 3 /s. One Bubble-Box data set was used to calibrate the acoustic backscatter response of the GasQuant data, enabling us to calculate a flow rate of the ensonified seep area (∼1,700 m 2 ) that ranged from 4.98 to 8.33 L/min (5.38 × 10 6 to 9.01 × 10 6 CH 4 mol/year). Such flow rates are common for seep areas of similar size, and as such, this location is classified as a normally active seep area. For deriving these acoustically based flow rates, the detailed data pre-processing considered echogram gridding methods of the swath data and bubble responses at the respective water depth. The described method uses the inverse gas flow quantification approach and gives an in-depth example of the benefits of using acoustic and optical methods in tandem.
    Type: Article , PeerReviewed
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  • 9
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    An Optical Digital Twin for Underwater Photogrammetry: GEODT - A Geometrically Verified Optical Digital Twin for Development, Evaluation, Training, Testing and Tuning of Multi-Media Refractive Algorithms (2022)
    Springer
    Details
    Publication Date: 2024-02-07
    Description: Most parts of the Earth’s surface are situated in the deep ocean. To explore this visually rather adversarial environment with cameras, they have to be protected by pressure housings. These housings, in turn, need interfaces to the world, enduring extreme pressures within the water column. Commonly, a flat window or a half-sphere of glass, called flat-port or dome-port, respectively is used to implement such kind of interface. Hence, multi-media interfaces, between water, glass and air are introduced, entailing refraction effects in the images taken through them. To obtain unbiased 3D measurements and to yield a geometrically faithful reconstruction of the scene, it is mandatory to deal with the effects in a proper manner. Hence, we propose an optical digital twin of an underwater environment, which has been geometrically verified to resemble a real water lab tank that features the two most common optical interfaces. It can be used to develop, evaluate, train, test and tune refractive algorithms. Alongside this paper, we publish the model for further extension, jointly with code to dynamically generate samples from the dataset. Finally, we also publish a pre-rendered dataset ready for use at https://git.geomar.de/david-nakath/geodt.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 10
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    Refractive Geometry for Underwater Domes (2022)
    Elsevier
    Details
    Publication Date: 2024-02-07
    Description: Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere’s center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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