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Conference Paper: Automated Hierarchical Image Segmentation Based on Merging of Quadrilaterals

TitleAutomated Hierarchical Image Segmentation Based on Merging of Quadrilaterals
Authors
KeywordsObject representations
Quadrilateral-based segmentation
Hierarchical merging
Feature of interest
Color
Issue Date2006
PublisherWSEAS.
Citation
The 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision (ISCGAV'06), Crete, Greece, August 2006. in Conference Proceedings, 2006, p. 135-140 How to Cite?
AbstractThis paper proposes a quadrilateral-based and automated hierarchical segmentation method, in which quadrilaterals are first constructed from an edge map, where neighboring quadrilaterals with similar features of interest are then merged together in a hierarchical mode to form regions. When evaluated qualitatively and quantitatively, the proposed method outperforms three traditional and commonly-used techniques, namely, K-means clustering, seeded region growing and quadrilateral-based segmentation. It is shown by experimental results that our proposed method is robust in both recovering missed important regions while preventing unnecessary over-segmentation, and offers an efficient description of the segmented objects conducive to content-based applications.
DescriptionProceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artifical Vision, 2006, p. 135-140
Persistent Identifierhttp://hdl.handle.net/10722/93168
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChen, Zen_HK
dc.contributor.authorChin, FYLen_HK
dc.contributor.authorChung, HYen_HK
dc.date.accessioned2010-09-25T14:52:57Z-
dc.date.available2010-09-25T14:52:57Z-
dc.date.issued2006en_HK
dc.identifier.citationThe 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision (ISCGAV'06), Crete, Greece, August 2006. in Conference Proceedings, 2006, p. 135-140en_HK
dc.identifier.isbn960-8457-51-3-
dc.identifier.urihttp://hdl.handle.net/10722/93168-
dc.descriptionProceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artifical Vision, 2006, p. 135-140-
dc.description.abstractThis paper proposes a quadrilateral-based and automated hierarchical segmentation method, in which quadrilaterals are first constructed from an edge map, where neighboring quadrilaterals with similar features of interest are then merged together in a hierarchical mode to form regions. When evaluated qualitatively and quantitatively, the proposed method outperforms three traditional and commonly-used techniques, namely, K-means clustering, seeded region growing and quadrilateral-based segmentation. It is shown by experimental results that our proposed method is robust in both recovering missed important regions while preventing unnecessary over-segmentation, and offers an efficient description of the segmented objects conducive to content-based applications.-
dc.languageengen_HK
dc.publisherWSEAS.en_HK
dc.relation.ispartofProceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artifical Visionen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectObject representations-
dc.subjectQuadrilateral-based segmentation-
dc.subjectHierarchical merging-
dc.subjectFeature of interest-
dc.subjectColor-
dc.titleAutomated Hierarchical Image Segmentation Based on Merging of Quadrilateralsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=960-8457-51-3&volume=&spage=135&epage=140&date=2006&atitle=Automated+Hierarchical+Image+Segmentation+Based+on+Merging+of+Quadrilaterals-
dc.identifier.emailChen, Z: zchen@cs.hku.hken_HK
dc.identifier.emailChin, FYL: chin@cs.hku.hken_HK
dc.identifier.emailChung, HY: hychung@cs.hku.hk-
dc.description.naturepostprint-
dc.identifier.hkuros132570en_HK
dc.identifier.spage135en_HK
dc.identifier.epage140en_HK
dc.description.otherThe 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision (ISCGAV'06), Crete, Greece, August 2006. in Conference Proceedings, 2006, p. 135-140-

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