Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2377
DC FieldValueLanguage
dc.contributor.authorKasparis, Takis-
dc.contributor.authorCharalampidis, Dimitrios-
dc.contributor.otherΚασπαρής, Τάκης-
dc.date.accessioned2013-02-15T10:44:36Zen
dc.date.accessioned2013-05-17T05:29:39Z-
dc.date.accessioned2015-12-02T11:21:35Z-
dc.date.available2013-02-15T10:44:36Zen
dc.date.available2013-05-17T05:29:39Z-
dc.date.available2015-12-02T11:21:35Z-
dc.date.issued2002-05-
dc.identifier.citationIEEE International Conference on Acoustic, Speech, and Signal Processing, 2002, Orlando, Floridaen_US
dc.identifier.issn1520-6149-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/2377-
dc.description.abstractIn this paper, we introduce a rotational invariant feature set for texture classification, based on an extension of fractal dimension (FD) features. The FD extracts roughness information from images considering all available scales at once. In this work, a single scale is considered at a time so that textures with scale-dependent properties are satisfactorily characterized. Single scale features are combined with multiple scale features for a more complete textural representation. Directional wavelets are employed for the computation of roughness features, because of their ability to extract information at different resolutions and directions. The final feature vector is rotational invariant and retains the texture directional information. The roughness feature set results in higher classification rate than other feature vectors presented in this work, while preserving the important properties of FD, namely insensitivity to absolute illumination and contrast.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2002 IEEEen_US
dc.subjectFractalsen_US
dc.subjectMathematical modelsen_US
dc.subjectImage analysisen_US
dc.subjectClassificationen_US
dc.titleRotation invariant roughness features for texture classificationen_US
dc.typeConference Papersen_US
dc.affiliationUniversity of New Orleansen
dc.collaborationUniversity of Central Floridaen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_US
dc.identifier.doi10.1109/ICASSP.2002.5745452en_US
dc.dept.handle123456789/54en
cut.common.academicyear2001-2002en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-3486-538x-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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