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Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/29076
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dc.contributor.authorAzzopardi, George-
dc.contributor.authorPetkov, Nicolai-
dc.date.accessioned2018-04-12T12:07:50Z-
dc.date.available2018-04-12T12:07:50Z-
dc.date.issued2013-
dc.identifier.citationAzzopardi, G., & Petkov, N. (2013). COSFIRE : a trainable features approach to pattern recognition. BENELEARN 2013, Nijmegen.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/29076-
dc.description.abstractIn a recent work (Azzopardi & Petkov, 2013), we proposed a trainable features approach to visual pattern recognition. It is called COSFIRE, which stands for Combination of Shifted Filter Responses. A COSFIRE operator is automatically configured by a specified pattern of interest, referred to as a prototype, and is then able to detect the same and similar patterns in other images. The configuration comprises the determination of the orientations of dominant contour parts and their mutual spatial arrangement.en_GB
dc.language.isoenen_GB
dc.publisherRadboud University Nijmegenen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectPattern recognition systemsen_GB
dc.subjectComputer visionen_GB
dc.subjectComputer graphicsen_GB
dc.subjectData structures (Computer science)en_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectOptical pattern recognitionen_GB
dc.titleCOSFIRE : a trainable features approach to pattern recognitionen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameBENELEARN 2013en_GB
dc.bibliographicCitation.conferenceplaceNijmegen, the Netherlands, 3/06/2013en_GB
dc.description.reviewedpeer-revieweden_GB
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