Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2935| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Shorter, Nicholas S. | - |
| dc.contributor.author | Kasparis, Takis | - |
| dc.contributor.other | Κασπαρής, Τάκης | - |
| dc.date.accessioned | 2010-02-18T06:54:26Z | en |
| dc.date.accessioned | 2013-05-17T05:34:09Z | - |
| dc.date.accessioned | 2015-12-02T12:27:21Z | - |
| dc.date.available | 2010-02-18T06:54:26Z | en |
| dc.date.available | 2013-05-17T05:34:09Z | - |
| dc.date.available | 2015-12-02T12:27:21Z | - |
| dc.date.issued | 2008-12 | - |
| dc.identifier.citation | SSPR /SPR 2008: Structural, Syntactic, and Statistical Pattern Recognition, pp. 147-156, Orlando, USA, December 4-6, 2008. Proceedings | en_US |
| dc.identifier.isbn | 9783540896883 | - |
| dc.identifier.issn | 0302-9743 (Print) | - |
| dc.identifier.issn | 1611-3349 (Online) | - |
| dc.description | Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342) | en_US |
| dc.description.abstract | The use of Fuzzy Adaptive Resonance Theory (FA) is explored for the unsupervised color quantization of a color image. The red, green and blue color component values of a given color image are passed as input instances into FA which then groups similar colors into the same class. The average of all of the colors in a given class then replaces the pixel values whose original colors belonged to that class. The FA unsupervised clustering is capable of realizing color quantization with competitive accuracy and arguably low computation time. | en_US |
| dc.format | en_US | |
| dc.language.iso | en | en_US |
| dc.rights | © Springer | en_US |
| dc.subject | Image Color Quantization | en_US |
| dc.subject | Fuzzy ART | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Unsupervised | en_US |
| dc.title | Fuzzy ART for Relatively Fast Unsupervised Image Color Quantization | en_US |
| dc.type | Conference Papers | en_US |
| dc.affiliation | University of Central Florida | 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 | Structural, Syntactic, and Statistical Pattern Recognition | en_US |
| dc.identifier.doi | 10.1007/978-3-540-89689-0_19 | en_US |
| dc.dept.handle | 123456789/54 | en |
| cut.common.academicyear | 2008-2009 | en_US |
| item.languageiso639-1 | en | - |
| item.cerifentitytype | Publications | - |
| item.fulltext | No Fulltext | - |
| item.grantfulltext | none | - |
| item.openairetype | conferenceObject | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
| 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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