Journal articles


Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Timm Schoening, Melanie Bergmann, Jörg Ontrup, James Taylor, Jennifer Dannheim, et al. (2012)

Abstract: 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.

Citation: Schoening T, Bergmann M, Ontrup J, Taylor J, Dannheim J, et al. (2012) Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN. PLoS ONE 7(6): e38179. doi:10.1371/journal.pone.0038179

Fully automated segmentation of compact multi-component objects in underwater images with the ES4C algorithm

Timm Schoening, Thomas Kuhn, Tim W Nattkemper (in review)


DELPHI - fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections

Timm Schoening, Thomas Kuhn, Melanie Bergmann, Tim W Nattkemper (2015)

Citation: Schoening, T., Kuhn, T., Bergmann, M., & Nattkemper, T. W. (2015). DELPHI—fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections. Frontiers in Marine Science, 2, 20.

Microhabitat and shrimp abundance within a Norwegian cold-water coral ecosystem

Autun Purser, Jörg Ontrup, Timm Schoening, Tim W. Nattkemper, et.al. (2013)
Citation: Purser, A., Ontrup, J., Schoening, T., Thomsen, L., Tong, R., Unnithan, V., and Nattkemper, T. W.: Microhabitat and shrimp abundance within a Norwegian cold-water coral ecosystem, Biogeosciences Discuss., 10, 3365-3396, doi:10.5194/bgd-10-3365-2013, 2013.

Conferences


Seabed classification using a bag-of-prototypes feature representation

Timm Schoening, Thomas Kuhn, Tim W Nattkemper (2014)

CVAIU @ ICPR 2014


Image-based Marine Resource Exploration and Biodiversity Assessment with MAMAS (Marine data Asset Management and Analysis System)

Tim W Nattkemper, Timm Schoening, Daniel Brün (2014)

UMI 2014


Ultra-fast segmentation and quantification of poly-metallic nodule coverage in high resolution digital images

Timm Schoening, Björn Steinbrink, Daniel Brün, Thomas Kuhn, Tim W. Nattkemper (2013)

UMI 2013


Application of Hydro-Acoustic and Video Data for the Exploration of Manganese Nodule Fields

Thomas Kuhn, Carsten Rühlemann, Michael Wiedicke-Hombach, Timm Schoening, Tim W. Nattkemper (2013)

ISOPE 2013


A machine-learning system for the automated detection of megafauna and its applicability to unseen footage

Timm Schoening, Melanie Bergmann, Tim W. Nattkemper (2013)

GEOHAB 2013


The impact of human expert knowledge on automated object detection in benthic images

Timm Schoening, Melanie Bergmann, Tim W. Nattkemper (2012)

DSBS 2012


Investigation of hidden parameters influencing the automated object detection in images from the deep seafloor of the HAUSGARTEN observatory

Timm Schoening, Melanie Bergmann, Anthe Boetius, Tim W. Nattkemper (2012)

OCEANS 2012


Estimation of poly-metallic nodule coverage in benthic images

Timm Schoening, Thomas Kuhn, Tim W. Nattkemper (2012)

UMI 2012


Towards improved epilepsia diagnosis by unsupervised segmentation of neuropathological tissue sections using Ripley's-L features

Timm Schoening, Volkmar Hans, Tim W. Nattkemper (2011)

BVM 2011


Ein neues Computer-gestütztes Bildanalyseverfahren zur Diagnose und Erforschung der Epilepsie

Timm Schoening, Volkmar Hans, Tim W. Nattkemper (2010)

SF 2010


Biigle Tools - A Web 2.0 approach for Visual Bioimage Database Mining

Timm Schoening, Nils Ehnert, Jörg Ontrup, Tim W. Nattkemper (2009)

IV 2009