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dc.contributor.authorAndrade-Loarca, Héctor
dc.contributor.authorKutyniok, Gitta Astrid Hildegard
dc.contributor.authorÖktem, Ozan
dc.date.accessioned2022-04-12T07:33:49Z
dc.date.available2022-04-12T07:33:49Z
dc.date.issued2020-11-25
dc.description.abstractSemantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.en_US
dc.identifier.citationAndrade-Loarca, Kutyniok, Öktem. Shearlets as feature extractor for semantic edge detection: The model-based and data-driven realm: Shearlets for Semantic Edge Detection. Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences. 2020;476(2243):1-26en_US
dc.identifier.cristinIDFRIDAID 1890140
dc.identifier.doi10.1098/rspa.2019.0841
dc.identifier.issn1364-5021
dc.identifier.issn1471-2946
dc.identifier.urihttps://hdl.handle.net/10037/24760
dc.language.isoengen_US
dc.publisherThe Royal Societyen_US
dc.relation.journalProceedings of the Royal Society. Mathematical, Physical and Engineering Sciences
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.titleShearlets as feature extractor for semantic edge detection: The model-based and data-driven realm: Shearlets for Semantic Edge Detectionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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