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dc.contributor.authorLee, KLen_US
dc.contributor.authorChen, LHen_US
dc.date.accessioned2014-12-08T15:43:11Z-
dc.date.available2014-12-08T15:43:11Z-
dc.date.issued2001-12-01en_US
dc.identifier.issn0218-0014en_US
dc.identifier.urihttp://dx.doi.org/10.1142/S0218001401001416en_US
dc.identifier.urihttp://hdl.handle.net/11536/29236-
dc.description.abstractTraditional approaches for texture segmentation via wavelet transform usually adopt textural features to achieve segmentation purposes. However, for a natural image, the characteristics of the pixels in a texture region are not similar everywhere from a global viewpoint, and over-segmentation often occurs. To deal with this issue, an unsupervised texture segmentation method based on determining the interior of texture regions is proposed. The key idea of the proposed method is that if the pixels of the input image can be classified into interior pixels (pixels within a texture region) and boundary ones, then the segmentation can be achieved by applying region growing on the interior pixels and reclassifying boundary pixels. Based on the fact that each pixel P within a texture region will have similar characteristics with its neighbors, after applying wavelet transform, pixel P will have similar response with its neighbors in each transformed subimage. Thus, by applying a multilevel thresholding technique to segment each subimage into several regions, pixel P and its neighbors will be assigned to the same region in most subimages. Based on these segmented results, an interior pixels finding algorithm is then provided to find all interior pixels of textural regions. The algorithm considers a pixel which is in the same region as its neighbors in most subimages as an interior pixel. The effectiveness of this method is proved by successfully segmenting natural texture images and comparing with other methods.en_US
dc.language.isoen_USen_US
dc.subjecttexture segmentationen_US
dc.subjectwavelet transformen_US
dc.subjectmultiresolution segmentationen_US
dc.subjectunsupervised clusteringen_US
dc.titleUnsupervised texture segmentation by determining the interior of texture regions based on wavelet transformen_US
dc.typeArticleen_US
dc.identifier.doi10.1142/S0218001401001416en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCEen_US
dc.citation.volume15en_US
dc.citation.issue8en_US
dc.citation.spage1231en_US
dc.citation.epage1250en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000173461000002-
dc.citation.woscount6-
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