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  • 1
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    Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification (2008)
    Oxford University Press
    Details
    Publication Date: 2008-06-05
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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    PAPER CURRENT
  • 2
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    The Cyborg Astrobiologist: porting from a wearable computer to the Astrobiology Phone-cam (2007)
    Cambridge University Press
    Details
    Publication Date: 2007-08-22
    Description: We have used a simple camera phone to significantly improve an ‘exploration system’ for astrobiology and geology. This camera phone will make it much easier to develop and test computer-vision algorithms for future planetary exploration. We envision that the ‘Astrobiology Phone-cam’ exploration system can be fruitfully used in other problem domains as well.
    Print ISSN: 1473-5504
    Electronic ISSN: 1475-3006
    Topics: Biology , Geosciences
    Published by Cambridge University Press
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  • 3
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    The Cyborg Astrobiologist: scouting red beds for uncommon features with geological significance (2005)
    Cambridge University Press
    Details
    Publication Date: 2005-10-04
    Print ISSN: 1473-5504
    Electronic ISSN: 1475-3006
    Topics: Biology , Geosciences
    Published by Cambridge University Press
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  • 4
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    The Cyborg Astrobiologist: first field experience (2004)
    Cambridge University Press
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    Publication Date: 2004-07-01
    Description: We present results from the first geological field tests of the ‘Cyborg Astrobiologist’, which is a wearable computer and video camcorder system that we are using to test and train a computer-vision system towards having some of the autonomous decision-making capabilities of a field-geologist and field-astrobiologist. The Cyborg Astrobiologist platform has thus far been used for testing and development of the following algorithms and systems: robotic acquisition of quasi-mosaics of images; real-time image segmentation; and real-time determination of interesting points in the image mosaics. The hardware and software systems function reliably, and the computer-vision algorithms are adequate for the first field tests. In addition to the proof-of-concept aspect of these field tests, the main result of these field tests is the enumeration of those issues that we can improve in the future, including: detection and accounting for shadows caused by three-dimensional jagged edges in the outcrop; reincorporation of more sophisticated texture-analysis algorithms into the system; creation of hardware and software capabilities to control the camera's zoom lens in an intelligent manner; and, finally, development of algorithms for interpretation of complex geological scenery. Nonetheless, despite these technical inadequacies, this Cyborg Astrobiologist system, consisting of a camera-equipped wearable-computer and its computer-vision algorithms, has demonstrated its ability in finding genuinely interesting points in real-time in the geological scenery, and then gathering more information about these interest points in an automated manner.
    Print ISSN: 1473-5504
    Electronic ISSN: 1475-3006
    Topics: Biology , Geosciences
    Published by Cambridge University Press
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  • 5
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    Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN (2012)
    Public Library of Science
    In:  PLoS ONE, 7 (6). e38179.
    Details
    Publication Date: 2017-07-18
    Description: Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter-and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i.e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.
    Type: Article , PeerReviewed
    Format: text
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  • 6
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    BIIGLE Tools - A Web 2.0 Approach for Visual Bioimage Database Mining (2009)
    In:  [Paper] In: 13. International Conference on Information Visualization, 15.-17.07.2009, Barcelona, Spain ; pp. 51-56 .
    Details
    Publication Date: 2018-03-15
    Description: In this paper we want to discuss the usage of web 2.0 techniques to realize information visualization based exploration and annotation of huge volume, semi-structured data, and in particular high throughput bioimage series. To this end, we developed a toolbox for a graphical representation of different displays in a browser context which can be used for image database exploration in a link & brush fashion. The web based approach proved to be capable of information visualization tasks and supports collaboration of several users at arbitrary locations.
    Type: Conference or Workshop Item , NonPeerReviewed
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  • 7
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    (Table 2) Species density and composition of an inshore and offshore station in Kongsfjord, Svalbard (2011)
    PANGAEA
    In:  Supplement to: Bergmann, Melanie; Langwald, Nina; Ontrup, Jörg; Soltwedel, Thomas; Schewe, Ingo; Klages, Michael; Nattkemper, Tim W (2011): Megafaunal assemblages from two shelf stations west of Svalbard. Marine Biology Research, 7(6), 525-539, https://doi.org/10.1080/17451000.2010.535834
    Details
    Publication Date: 2023-12-13
    Description: Megafauna plays an important role in benthic ecosystems and contributes significantly to benthic biomass in the Arctic. The distribution is mostly studied using towed cameras. Here, we compare the megafauna from two sites located at different distances from the Kongsfjord: one station at the entrance to the fjord, another on the outer shelf. Although they are only located 25 km apart and at comparable depth, there were significant differences in their species composition. While the inshore station was characterized by shrimps (2.57 +/- 2.18 ind./m**2) and brittlestars (3.21 +/- 3.21 ind./m**2), the offshore site harboured even higher brittlestar densities (15.23 +/- 9.32 ind./m**2) and high numbers of the sea urchin Strongylocentrotus pallidus (1.23 +/- 1.09 ind./m**2). Phytodetrital concentrations of the upper sediment centimetres were significantly higher inshore compared with offshore. At a smaller scale, there were also differences in the composition of different transect sections. Several taxa were characterized by a patchy distribution along transects. We conclude that these differences were caused primarily by habitat characteristics. The seafloor inshore was characterized by glacial soft sediments, whereas the station offshore harboured large quantities of stones. Although the use of a new web-2.0-based tool, BIIGLE (http://www.BIIGLE.de), allowed us to analyse more images (~90) than could have been achieved by hand, taxon area curves indicated that the number of images analysed was not sufficient to capture the species inventory fully. New automated image analysis tools would enable a rapid analysis of larger quantities of camera footage.
    Keywords: Actiniaria; Actiniaria, standard deviation; Actiniidae; Actinostolidae; Alcyonacea; Alcyonacea, standard deviation; Amblyraja radiata; Amblyraja radiata, standard deviation; Amphicteis gunneri; Amphipoda; Amphipoda, standard deviation; Anarhichas minor; Anarhichas minor, standard deviation; Anobothrus gracilis; Anthozoa; Anthozoa, standard deviation; Area/locality; Aristias tumidus; ARK-XXIII/2; Arrhis phyllonyx; Artacama proboscidea; Artediellus atlanticus; Artemisina apollinis; Artemisina apollinis, standard deviation; Ascidiacea; Ascidiacea, standard deviation; Astarte montagui; Asteroidea; Asteroidea, standard deviation; AWI; Brada granulosa; Brada inhabilis; Branchiomma sp.; Bylgides elegans; Bylgides groenlandicus; Capitella capitata; Caridea; Caridea, standard deviation; Ceriantharia; Ceriantharia, standard deviation; Chaetozone spp.; Chirimia biceps; Chlamys islandica; Chone sp.; Ciliatocardium ciliatum; Cirratulus sp.; Colossendeis proboscidea; Colossendeis proboscidea, standard deviation; Colus sabini; Coryphella salmonacea; Crossaster papposus; Crossaster papposus, standard deviation; Crustacea; Crustacea, standard deviation; Cryptonatica affinis; Ctenodiscus crispatus; Cylichna sp.; Dendrobeania cf. fruticosa; Eteone flava; Eteone foliosa; Eunoe nodosa; Euphrosine sp.; Eupyrgus scaber; Event label; Frigidoalvania janmayeni; Gadus morhua; Gadus morhua, standard deviation; Gastropoda; Gastropoda, standard deviation; Gattyana cirrhosa; Gersemia rubiformis; Gersemia rubiformis, standard deviation; Golfingia margaritacea; Gymnelus sp.; Halecium muricatum; Halecium scutum; Halirages fulvocincta; Haploops sp.; Harmothoe sp.; Henricia perforata; Heteromastus filiformis; Hiatella sp.; Hippoglossoides platessoides; Hippoglossoides platessoides, standard deviation; Hormathia digitata; Hormathia nodosa; Hyas spp.; Hyas spp., standard deviation; Icasterias panopla; Icasterias panopla, standard deviation; International Polar Year (2007-2008); IPY; Jasmineira cf. schaudinni; Laonice cf. cirrata; Laonice sp.; Leitoscoloplos mammosus; Lepeta caeca; Leptochiton sp.; Lumbrinereidae; Lumpenus lampretaeformis; Lumpenus lampretaeformis, standard deviation; Lycodes gracilis; Lysippe labiata; Maldane cf. arctica; Maldane sarsi; Maldanidae; Method comment; MF; Microcionidae; Multi frame; Munnopsis typica; Myriapora coarctata; Myriapora coarctata, standard deviation; Myriochele cf. oculata; Myriochele heeri; Myxilla sp.; Nemertea; Neoamphitrite affinis; Nephasoma diaphanes; Nephtys ciliata; Neptunea despecta; Nicomache lumbricalis; Nothria conchylega; Nuculana pernula; Nymphon hirtipes; Oedicerotidae; Oenopota sp.; OFOS photographic survey with BIIGLE analysis; Ophiacantha bidentata; Ophiopholis aculeata; Ophiura robusta; Ophiura sarsi; Ophiuroidea; Ophiuroidea, standard deviation; Pandalus sp.; Paramphithoe hystrix; Pedicellaster typicus; Phascolion strombi; Pherusa sp.; Philine finmarchica; Pholoe cf. assimilis; Phoxocephalus holbolli; Pisces; Pisces, standard deviation; Polarstern; Polynoidae; Porifera; Porifera, standard deviation; Praxillura longissima; Prionospio sp.; PS72; PS72/106-4; PS72/107-4; Pteraster cf. pulvillus; Sabellidae; Sclerocrangon sp.; Scoletoma fragilis; Sepiolidae; Serpulidae; Serpulidae, standard deviation; Similipecten greenlandicus; Solariella obscura; Spiochaetopterus typicus; Spiophanes kroeyeri; Stegocephalopsis ampulla; Stegopoma plicatile; Strongylocentrotus pallidus; Strongylocentrotus pallidus, standard deviation; Syllis cornuta; Tachyrhynchus reticulatus; Tedania suctoria; Terebellides sp.; Themisto sp.; Volutopsius norwegicus; Yoldiella propinqua; Yoldiella solidula; Zoarcidae; Zoarcidae, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 392 data points
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