GLORIA

GEOMAR Library Ocean Research Information Access

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • PANGAEA  (82)
  • Frontiers  (5)
Document type
Keywords
Publisher
Years
  • 1
    facet.materialart.
    Unknown
    RecoMIA - Recommendations for Marine Image Annotation: Lessons Learned and Future Directions (2016)
    Frontiers
    In:  Frontiers in Marine Science, 3 (59).
    Details
    Publication Date: 2019-09-23
    Description: Marine imaging is transforming into a sensor technology applied for high throughput sampling. In the context of habitat mapping, imaging establishes thereby an important bridge technology regarding the spatial resolution and information content between physical sampling gear (e.g., box corer, multi corer) on the one end and hydro-acoustic sensors on the other end of the spectrum of sampling methods. In contrast to other scientific imaging domains, such as digital pathology, there are no protocols and reports available that guide users (often referred to as observers) in the non-trivial process of assigning semantic categories to whole images, regions, or objects of interest (OOI), which is referred to as annotation. These protocols are crucial to facilitate image analysis as a robust scientific method. In this article we will review the past observations in manual Marine Image Annotations (MIA) and provide (a) a guideline for collecting manual annotations, (b) definitions for annotation quality, and (c) a statistical framework to analyze the performance of human expert annotations and to compare those to computational approaches.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.3389/fmars.2016.00059
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 2
    facet.materialart.
    Unknown
    BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections (2017)
    Frontiers
    In:  Frontiers in Marine Science, 4 (83).
    Details
    Publication Date: 2020-11-23
    Description: Combining state-of-the art digital imaging technology with different kinds of marine exploration techniques such as modern AUV (autonomous underwater vehicle), ROV (remote operating vehicle) or other monitoring platforms enables marine imaging on new spatial and/or temporal scales. A comprehensive interpretation of such image collections requires the detection, classification and quantification of objects of interest in the images usually performed by domain experts. However, the data volume and the rich content of the images makes the support by software tools inevitable. We define some requirements for marine image annotation and present our new online tool Biigle 2.0. It is developed with a special focus on annotating benthic fauna in marine image collections with tools customized to increase efficiency and effectiveness in the manual annotation process. The software architecture of the system is described and the special features of Biigle 2.0 are illustrated with different use-cases and future developments are discussed.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Format: text
    DOI: 10.3389/fmars.2017.00083
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
    facet.materialart.
    Unknown
    DELPHI - fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections (2015)
    Frontiers
    In:  Frontiers in Marine Science, 2 .
    Details
    Publication Date: 2018-01-04
    Description: Marine researchers continue to create large quantities of benthic images e.g., using AUVs (Autonomous Underwater Vehicles). In order to quantify the size of sessile objects in the images, a pixel-to-centimeter ratio is required for each image, often indirectly provided through a geometric laser point (LP) pattern, projected onto the seafloor. Manual annotation of these LPs in all images is too time-consuming and thus infeasible for nowadays data volumes. Because of the technical evolution of camera rigs, the LP's geometrical layout and color features vary for different expeditions and projects. This makes the application of one algorithm, tuned to a strictly defined LP pattern, also ineffective. Here we present the web-tool DELPHI, that efficiently learns the LP layout for one image transect/collection from just a small number of hand labeled LPs and applies this layout model to the rest of the data. The efficiency in adapting to new data allows to compute the LPs and the pixel-to-centimeter ratio fully automatic and with high accuracy. DELPHI is applied to two real-world examples and shows clear improvements regarding reduction of tuning effort for new LP patterns as well as increasing detection performance.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.3389/fmars.2015.00020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
    facet.materialart.
    Unknown
    SHiPCC - A Sea-going High-Performance Compute Cluster for Image Analysis (2019)
    Frontiers
    Details
    Publication Date: 2022-11-14
    Description: Marine image analysis faces a multitude of challenges: data set size easily reaches Terabyte-scale; the underwater visual signal is often impaired to the point where information content becomes negligible; human interpreters are scarce and can only focus on subsets of the available data due to the annotation effort involved etc. Solutions to speed-up the analysis process have been presented in the literature in the form of semi-automation with artificial intelligence methods like machine learning. But the algorithms employed to automate the analysis commonly rely on large-scale compute infrastructure. So far, such an infrastructure has only been available on-shore. Here, a mobile compute cluster is presented to bring big image data analysis capabilities out to sea. The Sea-going High-Performance Compute Cluster (SHiPCC) units are mobile, robustly designed to operate with electrically impure ship-based power supplies and based on off-the-shelf computer hardware. Each unit comprises of up to eight compute nodes with graphics processing units for efficient image analysis and an internal storage to manage the big image data sets. The first SHiPCC unit has been successfully deployed at sea. It allowed us to extract semantic and quantitative information from a Terabyte-sized image data set within 1.5 h (a relative speedup of 97 compared to a single four-core CPU computer). Enabling such compute capability out at sea allows to include image-derived information into the cruise research plan, for example by determining promising sampling locations. The SHiPCC units are envisioned to generally improve the relevance and importance of optical imagery for marine science.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
    facet.materialart.
    Unknown
    Monitoring of Anthropogenic Sediment Plumes in the Clarion-Clipperton Zone, NE Equatorial Pacific Ocean (2022)
    Frontiers
    Details
    Publication Date: 2024-02-07
    Description: The abyssal seafloor in the Clarion-Clipperton Zone (CCZ) in the NE Pacific hosts the largest abundance of polymetallic nodules in the deep sea and is being targeted as an area for potential deep-sea mining. During nodule mining, seafloor sediment will be brought into suspension by mining equipment, resulting in the formation of sediment plumes, which will affect benthic and pelagic life not naturally adapted to any major sediment transport and deposition events. To improve our understanding of sediment plume dispersion and to support the development of plume dispersion models in this specific deep-sea area, we conducted a small-scale, 12-hour disturbance experiment in the German exploration contract area in the CCZ using a chain dredge. Sediment plume dispersion and deposition was monitored using an array of optical and acoustic turbidity sensors and current meters placed on platforms on the seafloor, and by visual inspection of the seafloor before and after dredge deployment. We found that seafloor imagery could be used to qualitatively visualise the redeposited sediment up to a distance of 100 m from the source, and that sensors recording optical and acoustic backscatter are sensitive and adequate tools to monitor the horizontal and vertical dispersion of the generated sediment plume. Optical backscatter signals could be converted into absolute mass concentration of suspended sediment to provide quantitative data on sediment dispersion. Vertical profiles of acoustic backscatter recorded by current profilers provided qualitative insight into the vertical extent of the sediment plume. Our monitoring setup proved to be very useful for the monitoring of this small-scale experiment and can be seen as an exemplary strategy for monitoring studies of future, upscaled mining trials. We recommend that such larger trials include the use of AUVs for repeated seafloor imaging and water column plume mapping (optical and acoustical), as well as the use of in-situ particle size sensors and/or particle cameras to better constrain the effect of suspended particle aggregation on optical and acoustic backscatter signals.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
    facet.materialart.
    Unknown
    Raw data from Miniature Autonomous Plume recorder (MAPR) at stations during SONNE cruise SO242/1 (2018)
    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Details
    Publication Date: 2023-03-03
    Keywords: BC; Box corer; Calculated; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; EBS; Elevation of event; Epibenthic sledge; Event label; GC; Gravity corer; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; Julian day; LATITUDE; Light, backscattered from particles; LONGITUDE; Miniature Autonomous Plume Recorder (MAPR); MUC; MultiCorer; Number; Ocean Floor Observation System; OFOS; Optional event label; Oxidation reduction (RedOx) potential; Pressure, water; SO242/1; SO242/1_100-1; SO242/1_100-1_GC 5; SO242/1_101-1; SO242/1_101-1_BC 18; SO242/1_103-1; SO242/1_103-1_BC 19; SO242/1_104-1; SO242/1_104-1_EBS 6; SO242/1_108-1; SO242/1_108-1_MUC 26; SO242/1_109-1; SO242/1_109-1_MUC 27; SO242/1_1-1; SO242/1_1-1_CTD 1; SO242/1_110-1; SO242/1_110-1_MUC 28; SO242/1_111-1; SO242/1_111-1_OFOS 3; SO242/1_112-1; SO242/1_112-1_OFOS 4; SO242/1_115-1; SO242/1_115-1_MUC 30; SO242/1_117-1; SO242/1_117-1_EBS 7; SO242/1_119-1; SO242/1_119-1_MUC 31; SO242/1_120-1; SO242/1_120-1_BC 21; SO242/1_121-1; SO242/1_121-1_BC 22; SO242/1_122-1; SO242/1_122-1_EBS 8; SO242/1_123-1; SO242/1_123-1_GC 6; SO242/1_124-1; SO242/1_124-1_BC 23; SO242/1_126-1; SO242/1_126-1_EBS 9; SO242/1_127-1; SO242/1_127-1_BC 24; SO242/1_128-1; SO242/1_128-1_BC 25; SO242/1_129-1; SO242/1_129-1_BC 26; SO242/1_130-1; SO242/1_130-1_MUC 32; SO242/1_131-1; SO242/1_131-1_MUC 33; SO242/1_132-1; SO242/1_132-1_GC 7; SO242/1_134-1; SO242/1_134-1_OFOS 5; SO242/1_135-1; SO242/1_135-1_OFOS 6; SO242/1_19-1; SO242/1_19-1_MUC 1; SO242/1_20-1; SO242/1_20-1_BC 1; SO242/1_22-1; SO242/1_22-1_MUC 2; SO242/1_24-1; SO242/1_24-1_MUC 3; SO242/1_26-1; SO242/1_26-1_BC 2; SO242/1_27-1; SO242/1_27-1_BC 3; SO242/1_28-1; SO242/1_28-1_MUC 4; SO242/1_31-1; SO242/1_31-1_BC 4; SO242/1_32-1; SO242/1_32-1_BC 5; SO242/1_35-1; SO242/1_35-1_MUC 7; SO242/1_37-1; SO242/1_37-1_EBS 1; SO242/1_38-1; SO242/1_38-1_GC 1; SO242/1_39-1; SO242/1_39-1_MUC 8; SO242/1_40-1; SO242/1_40-1_MUC 9; SO242/1_43-1; SO242/1_43-1_OFOS 1; SO242/1_45-1; SO242/1_45-1_EBS 2; SO242/1_46-1; SO242/1_46-1_MUC 11; SO242/1_48-1; SO242/1_48-1_BC 6; SO242/1_49-1; SO242/1_49-1_BC 7; SO242/1_51-1; SO242/1_51-1_GC 2; SO242/1_52-1; SO242/1_52-1_BC 8; SO242/1_53-1; SO242/1_53-1_BC 9; SO242/1_54-1; SO242/1_54-1_BC 10; SO242/1_56-1; SO242/1_56-1_MUC 12; SO242/1_61-1; SO242/1_61-1_MUC 13; SO242/1_62-1; SO242/1_62-1_MUC 14; SO242/1_70-1; SO242/1_70-1_MUC 17; SO242/1_71-1; SO242/1_71-1_MUC 18; SO242/1_73-1; SO242/1_73-1_MUC 19; SO242/1_74-1; SO242/1_74-1_MUC 20; SO242/1_76-1; SO242/1_76-1_OFOS 2; SO242/1_77-1; SO242/1_77-1_BC 11; SO242/1_78-1; SO242/1_78-1_BC 12; SO242/1_79-1; SO242/1_79-1_MUC 21; SO242/1_80-1; SO242/1_80-1_MUC 22; SO242/1_84-1; SO242/1_84-1_GC 3; SO242/1_85-1; SO242/1_85-1_EBS 4; SO242/1_86-1; SO242/1_86-1_BC 13; SO242/1_87-1; SO242/1_87-1_BC 14; SO242/1_89-1; SO242/1_89-1_GC 4; SO242/1_90-1; SO242/1_90-1_MUC 23; SO242/1_91-1; SO242/1_91-1_MUC 24; SO242/1_92-1; SO242/1_92-1_MUC 25; SO242/1_93-1; SO242/1_93-1_EBS 5; SO242/1_95-1; SO242/1_95-1_BC 15; SO242/1_96-1; SO242/1_96-1_BC 16; SO242/1_98-1; SO242/1_98-1_BC 17; Sonne_2; Temperature, water; West Reference Area
    Type: Dataset
    Format: text/tab-separated-values, 1136933 data points
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 7
    facet.materialart.
    Unknown
    Raw data from Miniature Autonomous Plume recorder (MAPR) at stations during SONNE cruise SO242/2 (2018)
    PANGAEA
    In:  GEOMAR - Helmholtz Centre for Ocean Research Kiel
    Details
    Publication Date: 2023-03-03
    Keywords: Calculated; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Elevation of event; Event label; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; Julian day; LATITUDE; Light, backscattered from particles; LONGITUDE; Miniature Autonomous Plume Recorder (MAPR); MUC; MultiCorer; Number; Ocean Floor Observation System; OFOS; Optional event label; Oxidation reduction (RedOx) potential; Pressure, water; Remote operated vehicle; Remote operated vehicle elevator; ROV; ROV_E; SO242/2; SO242/2_138-1; SO242/2_139-1; SO242/2_142-1; SO242/2_151-1; SO242/2_153-1; SO242/2_154-1; SO242/2_155-1; SO242/2_157-1; SO242/2_163-1; SO242/2_166-1; SO242/2_169-1; SO242/2_172-1; SO242/2_176-1; SO242/2_179-1; SO242/2_183-1; SO242/2_188-1; SO242/2_190-1; SO242/2_191-1; SO242/2_194-1; SO242/2_196-1; SO242/2_198-1; SO242/2_202-1; SO242/2_205-1; SO242/2_207-1; SO242/2_208-1; SO242/2_211-1; SO242/2_213-1; SO242/2_216-1; SO242/2_219-1; SO242/2_222-1; SO242/2_224-1; SO242/2_232-1; SO242/2_235-1; Sonne_2; South Pacific Ocean, Peru Basin; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 1548428 data points
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 8
    facet.materialart.
    Unknown
    Seafloor images and raw context data along AUV track SO242/1_25-1_AUV03 (Abyss_189) during SONNE cruise SO242/1 (2017)
    PANGAEA
    Details
    Publication Date: 2023-03-28
    Description: The raw images (available on request) have been captured using a Canon 8-15mm fisheye lens and therefore they have a wide field of view, which results in a dark image boundary as the lights did not illuminate the outer sectors well. The images in this dataset have then been undistorted to virtual images that an ideal perspective camera with only 90 degrees horizontal field of view would have seen from the same position. To achieve this, the color of each pixel in the ideal image is obtained by - computing the ray in space associated with this virtual pixel (using rectilinear un-projection) - projecting this ray into the original fisheye image (using equidistant projection), yielding a sub-pixel position - interpolating the colors of the neighboring pixels Technically, the undistortion has been performed using the tool https://svn.geomar.de/dsm-general/trunk/src/BIAS/Tools/biasproject.cpp (at revision 418, and earlier, compatible revisions). Manual image annotation is available here: https://annotate.geomar.de/volumes/257
    Keywords: Acoustic Doppler Current Profiling (ADCP); Autonomous underwater vehicle; AUV; AUV forward velocity; AUV starboard velocity; AUV vertical velocity; Chlorophyll a; Conductivity; CTD, SEA-BIRD SBE 49; DATE/TIME; DEA; DEPTH, water; Digital camera, Canon EOS 6D, Fisheye lens; DISCOL Experimental Area; Distance; File format; File name; File size; Fluorometer, WET Labs, ECO FLNTU; Ground visibility (1=yes/0=no); Heading; Image brightness; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; LATITUDE; LONGITUDE; Pitch angle; Roll angle; Salinity; SO242/1; SO242/1_25-1; SO242/1_25-1_AUV 3; Sonne_2; Sound velocity in water; Temperature, water; Time, relative; Turbidity (Nephelometric turbidity unit); Uniform resource locator/link to image
    Type: Dataset
    Format: text/tab-separated-values, 67961 data points
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 9
    facet.materialart.
    Unknown
    Seafloor images and raw context data along AUV track SO242/1_33-1_AUV04 (Abyss_190) during SONNE cruise SO242/1 (2017)
    PANGAEA
    Details
    Publication Date: 2023-03-28
    Description: The raw images (available on request) have been captured using a Canon 8-15mm fisheye lens and therefore they have a wide field of view, which results in a dark image boundary as the lights did not illuminate the outer sectors well. The images in this dataset have then been undistorted to virtual images that an ideal perspective camera with only 90 degrees horizontal field of view would have seen from the same position. To achieve this, the color of each pixel in the ideal image is obtained by - computing the ray in space associated with this virtual pixel (using rectilinear un-projection) - projecting this ray into the original fisheye image (using equidistant projection), yielding a sub-pixel position - interpolating the colors of the neighboring pixels Technically, the undistortion has been performed using the tool https://svn.geomar.de/dsm-general/trunk/src/BIAS/Tools/biasproject.cpp (at revision 418, and earlier, compatible revisions). Manual image annotation is available here: https://annotate.geomar.de/volumes/258
    Keywords: Acoustic Doppler Current Profiling (ADCP); Autonomous underwater vehicle; AUV; AUV forward velocity; AUV starboard velocity; AUV vertical velocity; Chlorophyll a; Conductivity; CTD, SEA-BIRD SBE 49; DATE/TIME; DEA; DEPTH, water; Digital camera, Canon EOS 6D, Fisheye lens; DISCOL Experimental Area; Distance; File format; File name; File size; Fluorometer, WET Labs, ECO FLNTU; Ground visibility (1=yes/0=no); Heading; Image brightness; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; LATITUDE; LONGITUDE; Pitch angle; Roll angle; Salinity; SO242/1; SO242/1_33-1; SO242/1_33-1_AUV 4; Sonne_2; Sound velocity in water; Temperature, water; Time, relative; Turbidity (Nephelometric turbidity unit); Uniform resource locator/link to image
    Type: Dataset
    Format: text/tab-separated-values, 251089 data points
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 10
    facet.materialart.
    Unknown
    Seafloor images and raw context data along AUV track SO242/1_94-1_AUV12 (Abyss_198) during SONNE cruise SO242/1 (2017)
    PANGAEA
    Details
    Publication Date: 2023-03-28
    Description: The raw images (available on request) have been captured using a Canon 8-15mm fisheye lens and therefore they have a wide field of view, which results in a dark image boundary as the lights did not illuminate the outer sectors well. The images in this dataset have then been undistorted to virtual images that an ideal perspective camera with only 90 degrees horizontal field of view would have seen from the same position. To achieve this, the color of each pixel in the ideal image is obtained by - computing the ray in space associated with this virtual pixel (using rectilinear un-projection) - projecting this ray into the original fisheye image (using equidistant projection), yielding a sub-pixel position - interpolating the colors of the neighboring pixels Technically, the undistortion has been performed using the tool https://svn.geomar.de/dsm-general/trunk/src/BIAS/Tools/biasproject.cpp (at revision 418, and earlier, compatible revisions). Manual image annotation is available here: https://annotate.geomar.de/volumes/262
    Keywords: Acoustic Doppler Current Profiling (ADCP); Autonomous underwater vehicle; AUV; AUV forward velocity; AUV starboard velocity; AUV vertical velocity; Chlorophyll a; Conductivity; CTD, SEA-BIRD SBE 49; DATE/TIME; DEA; DEPTH, water; Digital camera, Canon EOS 6D, Fisheye lens; DISCOL Experimental Area; Distance; File format; File name; File size; Fluorometer, WET Labs, ECO FLNTU; Ground visibility (1=yes/0=no); Heading; Image brightness; JPI-OCEANS; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; LATITUDE; LONGITUDE; Pitch angle; Roll angle; Salinity; SO242/1; SO242/1_94-1; SO242/1_94-1_AUV 12; Sonne_2; Sound velocity in water; Temperature, water; Time, relative; Turbidity (Nephelometric turbidity unit); Uniform resource locator/link to image
    Type: Dataset
    Format: text/tab-separated-values, 480791 data points
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...