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dc.contributor.author
Wegmayr, Viktor
dc.contributor.author
Sahin, Aytunc
dc.contributor.author
Sæmundsson, Björn
dc.contributor.author
Buhmann, Joachim M.
dc.date.accessioned
2020-11-12T15:25:45Z
dc.date.available
2020-06-04T02:35:04Z
dc.date.available
2020-06-17T13:41:20Z
dc.date.available
2020-11-12T15:25:45Z
dc.date.issued
2020
dc.identifier.isbn
978-1-7281-6553-0
en_US
dc.identifier.isbn
978-1-7281-6552-3
en_US
dc.identifier.isbn
978-1-7281-6554-7
en_US
dc.identifier.other
10.1109/WACV45572.2020.9093352
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/418023
dc.description.abstract
Microplastics pollution has been recognized as a serious environmental concern, with research efforts underway to determine primary causes. Experiments typically generate bright-field images of microplastic fibers that are filtered from water. Environmental decision making in process engineering critically relies on accurate quantification of mi-croplastic fibers in these images. To satisfy the required standards, images are often analyzed manually, resulting in a highly tedious process, with thousands of fiber instances per image. While the shape of individual fibers is relatively simple, it is difficult to separate them in highly crowded scenes with significant overlap. We propose a fiber instance detection pipeline, which decomposes the fiber detection and segmentation into manageable sub-problems. Well separated instances are identified with robust image processing techniques, such as adaptive thresholding, and morphological skeleton analysis, while tangled fibers are separated by an algorithm based on deep pixel embeddings. Moreover, we present a modified Intersection-over-Union metric as a more appropriate similarity metric for elongated shapes. Our approach improves significantly on out-of-sample data, in particular for difficult cases of intersecting fibers. © 2020 IEEE.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Instance Segmentation for the Quantification of Microplastic Fiber Images
en_US
dc.type
Conference Paper
dc.date.published
2020-05-14
ethz.book.title
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
en_US
ethz.pages.start
2199
en_US
ethz.pages.end
2206
en_US
ethz.event
IEEE Winter Conference on Applications of Computer Vision (WACV 2020)
en_US
ethz.event.location
Snowmass Village, CO, USA
en_US
ethz.event.date
March 1-5, 2020
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.date.deposited
2020-06-04T02:35:14Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-06-17T13:41:40Z
ethz.rosetta.lastUpdated
2021-02-15T20:43:19Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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