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dc.contributor.authorTek, Faik Boraytr_TR
dc.contributor.authorCannavó, Flavioen_US
dc.contributor.authorNunnari, Giuseppeen_US
dc.contributor.authorKale, İzzet R.tr_TR
dc.date.accessioned2015-12-09T08:30:29Z
dc.date.available2015-12-09T08:30:29Z
dc.date.issued2012-11-02
dc.identifier.citationCannavo, F., Nunnari, G., Kale, I., & Tek, F. B. (2012). Texture recognition for frog identification. Paper presented at the 25-30. doi:10.1145/2390832.2390839en_US
dc.identifier.isbn9781450315883
dc.identifier.isbn1450315887
dc.identifier.urihttp://hdl.handle.net/11729/729
dc.identifier.urihttp://dx.doi.org/10.1145/2390832.2390839
dc.description.abstractThis paper describes a visual processing technique for automatic frog (Xenopus Laevis sp.) localization and identification. The problem of frog identification is to process and classify an unknown frog image to determine the identity which is recorded previously on an image database. The frog skin pattern (i.e. texture) provides a unique feature for identification. Hence, the study investigates three different kind of features (i.e. Gabor filters, granulometry, threshold set compactness) to extract texture information. The classifier is built on nearest neighbor principle; it assigns the query feature to the database feature which has the minimum distance. Hence, the study investigates different distance measures and compares their performance. The detailed results show that the most successful feature and distance measure is granulometry and weighted L1 norm for the frog identification using skin texture features.en_US
dc.language.isoenen_US
dc.publisherACM SIGMMen_US
dc.relation.isversionof10.1145/2390832.2390839
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.sourceMAED 2012 - Proceedings of the 2012 ACM Workshop on Multimedia Analysis for Ecological Data, Co-located with ACM Multimedia 2012en_US
dc.subjectFrog identificationen_US
dc.subjectImage processingen_US
dc.subjectDistance measureen_US
dc.subjectGranulometriesen_US
dc.subjectImage databaseen_US
dc.subjectLocalization and identificationen_US
dc.subjectMinimum distanceen_US
dc.subjectNearest neighborsen_US
dc.subjectSkin texturesen_US
dc.subjectTexture informationen_US
dc.subjectTexture recognitionen_US
dc.subjectUnique featuresen_US
dc.subjectVisual-processingen_US
dc.subjectXenopus laevisen_US
dc.subjectClassification (of information)en_US
dc.subjectEcologyen_US
dc.subjectQuery processingen_US
dc.subjectTexturesen_US
dc.titleTexture recognition for frog identificationen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisher's Versionen_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.contributor.departmentIsik University, Faculty of Engineering, Department of Computer Scienceen_US
dc.contributor.authorIDTR38373
dc.identifier.startpage25
dc.identifier.endpage30
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US


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