Learning precise local boundaries in images from human tracings

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HORN, Martin, Michael BERTHOLD, 2013. Learning precise local boundaries in images from human tracings. In: PETROSINO, Alfredo, ed.. Image Analysis and Processing – ICIAP 2013. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 131-140. ISBN 978-3-642-41180-9

@inproceedings{Horn2013Learn-26487, title={Learning precise local boundaries in images from human tracings}, year={2013}, doi={10.1007/978-3-642-41181-6_14}, number={8156}, isbn={978-3-642-41180-9}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Image Analysis and Processing – ICIAP 2013}, pages={131--140}, editor={Petrosino, Alfredo}, author={Horn, Martin and Berthold, Michael} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/26487"> <dcterms:bibliographicCitation>Image analysis and processing - ICIAP 2013 : 17th international conference ; Naples, Italy, September 9-13, 2013, Part I / Alfredo Petrosino (ed.). - Berlin : Springer, 2013. - S. 131-140. - (Lecture notes in computer science ; 8156). - ISBN 978-3-642-41180-9</dcterms:bibliographicCitation> <dcterms:title>Learning precise local boundaries in images from human tracings</dcterms:title> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/26487"/> <dc:contributor>Berthold, Michael</dc:contributor> <dcterms:issued>2013</dcterms:issued> <dcterms:abstract xml:lang="eng">Boundaries are the key cue to differentiate objects from each other and the background. However whether boundaries can be regarded as such cannot be determined generally as this highly depends on specific questions that need to be answered. As humans are best able to answer these questions and provide the required knowledge, it is often necessary to learn task-specific boundary properties from user-provided examples. However, current approaches to learning boundaries from examples completely ignore the inherent inaccuracy of human boundary tracings and, hence, derive an imprecise boundary description. We therefore provide an alternative view on supervised boundary learning and propose an efficient and robust algorithm to derive a precise boundary model for boundary detection.</dcterms:abstract> <dc:rights>deposit-license</dc:rights> <dc:creator>Berthold, Michael</dc:creator> <dc:language>eng</dc:language> <dc:creator>Horn, Martin</dc:creator> <dc:contributor>Horn, Martin</dc:contributor> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-03-26T12:28:40Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-03-26T12:28:40Z</dcterms:available> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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