Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2377
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kasparis, Takis | - |
dc.contributor.author | Charalampidis, Dimitrios | - |
dc.contributor.other | Κασπαρής, Τάκης | - |
dc.date.accessioned | 2013-02-15T10:44:36Z | en |
dc.date.accessioned | 2013-05-17T05:29:39Z | - |
dc.date.accessioned | 2015-12-02T11:21:35Z | - |
dc.date.available | 2013-02-15T10:44:36Z | en |
dc.date.available | 2013-05-17T05:29:39Z | - |
dc.date.available | 2015-12-02T11:21:35Z | - |
dc.date.issued | 2002-05 | - |
dc.identifier.citation | IEEE International Conference on Acoustic, Speech, and Signal Processing, 2002, Orlando, Florida | en_US |
dc.identifier.issn | 1520-6149 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/2377 | - |
dc.description.abstract | In 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.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © 2002 IEEE | en_US |
dc.subject | Fractals | en_US |
dc.subject | Mathematical models | en_US |
dc.subject | Image analysis | en_US |
dc.subject | Classification | en_US |
dc.title | Rotation invariant roughness features for texture classification | en_US |
dc.type | Conference Papers | en_US |
dc.affiliation | University of New Orleans | en |
dc.collaboration | University of Central Florida | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.country | United States | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | en_US |
dc.identifier.doi | 10.1109/ICASSP.2002.5745452 | en_US |
dc.dept.handle | 123456789/54 | en |
cut.common.academicyear | 2001-2002 | en_US |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-3486-538x | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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