00000000nam 2200000za 4500
0019.821643
003CaOODSP
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008160726s2011    nsc|||||o    f|0| 0 eng d
040 |aCaOODSP|beng
043 |an-cn---
0861 |aM183-2/6752E-PDF
24500|aAutomatic image analysis of sediment types |h[electronic resource] : |bmapping from georeferenced video footage on the Labrador Shelf / |cK. Jerosch ... [et al.].
260 |aDartmouth, N.S. : |bNatural Resources Canada, Geological Survey of Canada, |c2011.
300 |aiii, 33 p. : |btables, figures.
4901 |aOpen file (Geological Survey of Canada)|v6752
504 |aIncludes bibliographical references.
520 |aThis Open File describes mapping sea floor features efficiently from video footage providing a guideline for every required step. This is achieved by 1. developing a method to extract single frames from the video footage and make them suitable for Geographical Information Systems (GIS) (georeferencing the frames automatically), and 2. applying the Geospatial Image Database and Analysis System GIDAS (Lüdtke et al., accepted) on HD video frames from the Labrador Shelf, GIDAS provides an approach for a fully automatic detection and the quantification of sediment texture on sea floor images/frames on the Labrador Shelf. This is performed based on the extraction of textural features from the images and a statistical classification of these features using machine learning techniques. The approach includes a quality control based on a cross validation of the predictions. 3. Furthermore, the mapping of the results and discussing GIDAS of being a suitable technique for sedimentological seabed mapping forms the concluding scope of the Open File report.
69207|2gccst|aSediments
69207|2gccst|aOcean floor
69207|2gccst|aPhotography
693 4|aMapping
693 4|aMarine geology
7001 |aJerosch, Kerstin.
7101 |aCanada. |bNatural Resources Canada.
7102 |aGeological Survey of Canada (Atlantic)
7102 |aBedford Institute of Oceanography.
7102 |aGeological Survey of Canada.
830#0|aOpen file (Geological Survey of Canada)|v6752|w(CaOODSP)9.506878
85640|qPDF|s12.27 MB|uhttps://publications.gc.ca/collections/collection_2016/rncan-nrcan/M183-2-6752-eng.pdf
986 |adoi:10.4095/ 288055