Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Chiho | - |
dc.contributor.author | You, Bum-Jae | - |
dc.contributor.author | Jeong, Mun-Ho | - |
dc.contributor.author | Kim, Hagbae | - |
dc.date.accessioned | 2024-01-21T00:02:30Z | - |
dc.date.available | 2024-01-21T00:02:30Z | - |
dc.date.created | 2021-09-02 | - |
dc.date.issued | 2008-01 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/133858 | - |
dc.description.abstract | Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. However, color segmentation suffers from color variations incurred by irregular illumination changes. We propose a reliable color modeling approach in hue-saturation-intensity (HSI) color space while considering intensity information by adopting the B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to intensity. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in the HS (hue-saturation) plane. The statistical characteristics contain the mean and standard deviation of hue and saturation with respect to intensity. They are mathematically expressed as four bar graphs. In order to fit the bar graphs to continuous curves, we use B-spline curve fitting procedure. From several experimental results, we verify that the proposed algorithm is successfully applied to color segmentation under various illumination conditions. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | FACE DETECTION | - |
dc.subject | SKIN | - |
dc.title | Color segmentation robust to brightness variations by using B-spline curve modeling | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.patcog.2007.04.011 | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.41, no.1, pp.22 - 37 | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 41 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 22 | - |
dc.citation.endPage | 37 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.identifier.wosid | 000250028700002 | - |
dc.identifier.scopusid | 2-s2.0-34548030243 | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | FACE DETECTION | - |
dc.subject.keywordPlus | SKIN | - |
dc.subject.keywordAuthor | color consistency | - |
dc.subject.keywordAuthor | color segmentation | - |
dc.subject.keywordAuthor | B-spline curve | - |
dc.subject.keywordAuthor | brightness variations | - |
dc.subject.keywordAuthor | color model | - |
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