Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/232
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorGao, H-
dc.creatorSiu, WC-
dc.creatorHou, CH-
dc.date.accessioned2014-12-11T08:22:49Z-
dc.date.available2014-12-11T08:22:49Z-
dc.identifier.issn1051-8215-
dc.identifier.urihttp://hdl.handle.net/10397/232-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectImage segmentationen_US
dc.subjectMarket extractionen_US
dc.subjectMorphologyen_US
dc.titleImproved techniques for automatic image segmentationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1273-
dc.identifier.epage1280-
dc.identifier.volume11-
dc.identifier.issue12-
dc.identifier.doi10.1109/76.974681-
dcterms.abstractMathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies. The segmentation process is divided into three basic steps, namely: simplification, marker extraction, and boundary decision. Simplification, which makes use of area morphology, removes unnecessary information from the image to make it easy to segment. Marker extraction identifies the presence of homogeneous regions. A new marker-extraction design is proposed in this paper. It is based on both luminance and color information. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified watershed algorithm. A new color distance is also defined for this algorithm. In both marker extraction and boundary decision, color measurement is used to replace grayscale measurement and L*a*b* color space is used to replace the more straightforward spaces such as the RGB color space and YUV color space.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on circuits and systems for video technology, Dec. 2001, v. 11, no. 12, p.1273-1280-
dcterms.isPartOfIEEE transactions on circuits and systems for video technology-
dcterms.issued2001-12-
dc.identifier.isiWOS:000173285700006-
dc.identifier.scopus2-s2.0-0035694085-
dc.identifier.eissn1558-2205-
dc.identifier.rosgroupidr10572-
dc.description.ros2001-2002 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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