Development of website to allow crowdsourcing data capture
Development of website to allow crowdsourcing data capture
MfN
MUSEUM FUR NATURKUNDE - LEIBNIZ-INSTITUT FUR EVOLUTIONS- UND BIODIVERSITATSFORSCHUNG AN DER HUMBOLDT-UNIVERSITAT ZU BERLIN
Research into crowdsourcing methodologies for NH collections
Research into crowdsourcing methodologies for NH collections
Identify which digital image data are most appropriate for use with crowdsourcing. This work will draw on
experience with other citizen science crowdsourcing efforts, such as the Zooniverse (www.zooniverse.org)
project.
Micro-Computed Tomography (Micro-CT) for NH collections
Micro-Computed Tomography (Micro-CT) for NH collections
Develop protocols and workflows for the rapid digitisation of collections (sample preparation, scanning
parameters, model creation). The resulting models will be displayed and disseminated through a web-based
framework which will allow the user to manipulate the 3D tomograms through a series of online tools that will be
created.
Crowdsourcing metadata enrichment of digital images
Crowdsourcing metadata enrichment of digital images
New methods for 3D digitisation of NH collections
New methods for 3D digitisation of NH collections
Research on different 3D techniques
Research on different 3D techniques
High Resolution 3D colour image acquisition
High Resolution 3D colour image acquisition
Complementary approaches (colour surface scanning, photogrammetry) will be developed in order to provide
complete information (3D and colour data) of the specimen. Collaborate with existing European projects such
as 3DCOFORM whose focus is on Cultural Heritage digitisation. This task will develop 3DCOFORM outputs to
enable their use with NH specimens.
Automatic processing (segmentation) of digital images
Automatic processing (segmentation) of digital images
Research and develop edge detection technology to locate and classify multiple regions of interest within images
of NH specimens. Using the principle that pixels in a segment are similar with respect to some characteristic or
computed property (e.g. colour, intensity, or texture), develop a method to semi-automatically detect, crop and
classify these regions of interest such that they can be subject to appropriate additional processing.
Automatic metadata capture
Automatic metadata capture
Develop software that will automatically identify properties of an image. These data “facets” will be automatically
captured without human intervention and provide categories of information that allow Users to easily search and
browse virtual collections more effectively.
Automated data collection from digital images
Automated data collection from digital images
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