TZI Literaturverzeichnis

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Journal Article: BibTeX citation key:  Luedtke2011a
A. Luedtke, K. Jerosch, O. Herzog and M. Schlueter, "Development of a machine learning technique for automatic analysis of seafloor image data: Case example Pogonophora coverage at mud volcanoes", Computers & Geosciences, 2011.
Added by: AG Bildverarbeitung Herzog 2011-06-28 15:24:03    Last edited by: AG Bildverarbeitung Herzog 2011-06-28 15:24:58
Categories: AG-KI, Digitale Wirklichkeit, DM, General, graduate school digital media, Journals - reviewed
Keywords: automatic image analysis, Haakon Mosby Mud Volcano, image classification, machine learning, Pogonophora recognition, supervised learning
Creators: Herzog, Jerosch, Luedtke, Schlueter
Collection: Computers & Geosciences

Peer reviewed
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Digital image processing provides powerful tools for fast and precise analysis of large image data sets in marine and geoscientific applications. Facing the increasing amount of georeferenced image and video data acquired by underwater platforms such as Remotely Operated Vehicles (ROVs), means of automatic analysis of the acquired image data are required. A new and fast developing application is the combination of video imagery and mosaicking techniques for seafloor habitat mapping. In this article we introduce an approach for fully automatic detection and quantification of Pogonophora coverage in seafloor video mosaics from mud volcanoes. The automatic recognition is based on textural features extracted from the raw image data and classification using machine learning techniques. A classification accuracy of up to 98.86% was achieved on the training data. The approach was extensively validated on a data set of more than 4000 seafloor video mosaics from the Haakon Mosby Mud Volcano.
Added by: AG Bildverarbeitung Herzog    Last edited by: AG Bildverarbeitung Herzog

(accepted for publication)
Added by: AG Bildverarbeitung Herzog

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