Image matching using colour edge cooccurrence histograms

Jia, W; Zhang, H; He, X; Wu, Q

Image matching using colour edge cooccurrence histograms

Jia, W

Zhang, H He, X

Wu, Q

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2006, 3 pp. 2413 - 2419
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2006, 3 pp. 2413 - 2419	en_US
dc.identifier.isbn	1424401003	en_US
dc.identifier.isbn	9781424401000	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2574
dc.description.abstract	In this paper, a novel colour edge cooccurrence histogram (CECH) method is proposed to match images by measuring similarities between their CECH histograms. Unlike the previous colour edge cooccurrence histogram proposed by Crandall et al. in [2], we only investigate those pixels which are located at the two sides of edge points in their gradient direction lines and at a distance away from the edge points. When measuring similarities between two CECH histograms, a newly proposed Gaussian weighted histogram intersection (GWHI) method is extended for this purpose. Both identical colour pairs and similar colour pairs are taken into account in our algorithm, and the weights are decided by the larger distance between two colour pairs involved in matching. The proposed algorithm is tested for matching vehicle number plate images captured under various illumination conditions. Experimental results demonstrate that the proposed algorithm can be used to compare images in real-time, and is robust to illumination variations and insensitive to the model images selected. © 2006 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0452657
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICSMC.2006.385225	en_US
dc.title	Image matching using colour edge cooccurrence histograms	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Taipei, Taiwan	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 - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

In this paper, a novel colour edge cooccurrence histogram (CECH) method is proposed to match images by measuring similarities between their CECH histograms. Unlike the previous colour edge cooccurrence histogram proposed by Crandall et al. in [2], we only investigate those pixels which are located at the two sides of edge points in their gradient direction lines and at a distance away from the edge points. When measuring similarities between two CECH histograms, a newly proposed Gaussian weighted histogram intersection (GWHI) method is extended for this purpose. Both identical colour pairs and similar colour pairs are taken into account in our algorithm, and the weights are decided by the larger distance between two colour pairs involved in matching. The proposed algorithm is tested for matching vehicle number plate images captured under various illumination conditions. Experimental results demonstrate that the proposed algorithm can be used to compare images in real-time, and is robust to illumination variations and insensitive to the model images selected. © 2006 IEEE.

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http://hdl.handle.net/10453/2574