Aurora image segmentation by combining patch and texture thresholding

Gao, X; Fu, R; Li, X; Tao, D; Zhang, B; Yang, H

Aurora image segmentation by combining patch and texture thresholding

Gao, X Fu, R Li, X Tao, D

Zhang, B Yang, H

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Publication Type:: Journal Article
Citation:: Computer Vision and Image Understanding, 2011, 115 (3), pp. 390 - 402
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Fu, R	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Zhang, B	en_US
dc.contributor.author	Yang, H	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	Computer Vision and Image Understanding, 2011, 115 (3), pp. 390 - 402	en_US
dc.identifier.issn	1077-3142	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15199
dc.description.abstract	The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy. © 2010 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	Computer Vision and Image Understanding	en_US
dc.relation.isbasedon	10.1016/j.cviu.2010.11.011	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Aurora image segmentation by combining patch and texture thresholding	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	115	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	115	en_US

Abstract:

The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy. © 2010 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/15199