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Image segmentation using adaptive tree-structured wavelet transform
Pun C.-M.; Zhu H.-M.
2009-12-01
Conference Name6th International Conference on Computer Graphics, Imaging and Visualization
Source PublicationProceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009
Pages290-294
Conference Date2009-12
Conference PlaceMacau
Abstract

In this paper we propose a novel approach for image segmentation using adaptive tree-structured wavelet transform for texture analysis. We first split the input image into NxN blocks, calculate the distances between neighbor blocks by the energy signatures of the wavelet coefficients of the adaptive tree-structured wavelet transform of each block. Then we merge blocks with smallest distances to form larger regions. The process will repeat until we got desired number of regions. Experimental results show that our proposed method outperforms the existing image segmentation method. © 2009 IEEE.

KeywordImage Segmentation Texture Analysis Treestructured Wavelet Transform
DOI10.1109/CGIV.2009.32
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Imaging Science & Photographic Technology
WOS IDWOS:000275127400049
Scopus ID2-s2.0-70549085986
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Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPun C.-M.
AffiliationUniversidade de Macau
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Pun C.-M.,Zhu H.-M.. Image segmentation using adaptive tree-structured wavelet transform[C], 2009, 290-294.
APA Pun C.-M.., & Zhu H.-M. (2009). Image segmentation using adaptive tree-structured wavelet transform. Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, 290-294.
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