Summit Navigator: A Novel Approach for Local Maxima Extraction

Dinh, TH; Phung, MD; Ha, QP

Summit Navigator: A Novel Approach for Local Maxima Extraction

Dinh, TH

Phung, MD

Ha, QP

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2020, 29 pp. 551 - 564
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Dinh, TH https://orcid.org/0000-0002-2317-0032	en_US
dc.contributor.author	Phung, MD https://orcid.org/0000-0002-8870-5114	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.available	2021-08-12T19:00:53Z
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2020, 29 pp. 551 - 564	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135675
dc.description.abstract	© 1992-2012 IEEE. This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2019.2932501	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Summit Navigator: A Novel Approach for Local Maxima Extraction	en_US
dc.type	Journal Article
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 1992-2012 IEEE. This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.

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http://hdl.handle.net/10453/135675