Comparison of techniques for mitigating the effects of illumination variations on the appearance of human targets

Madden, C; Piccardi, M; Zuffi, S

Comparison of techniques for mitigating the effects of illumination variations on the appearance of human targets

Madden, C Piccardi, M

Zuffi, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4842 LNCS (PART 2), pp. 116 - 127
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Madden, C	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.author	Zuffi, S	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4842 LNCS (PART 2), pp. 116 - 127	en_US
dc.identifier.isbn	9783540768555	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2725
dc.description.abstract	Several techniques have been proposed to date to build colour invariants between camera views with varying illumination conditions. In this paper, we propose to improve colour invariance by using data-dependent techniques. To this aim, we compare the effectiveness of histogram stretching, illumination filtration, full histogram equalisation and controlled histogram equalisation in a video surveillance domain. All such techniques have limited computational requirements and are therefore suitable for real time implementation. Controlled histogram equalisation is a modified histogram equalisation operating under the influence of a control parameter [1]. Our empirical comparison looks at the ability of these techniques to make the global colour appearance of single human targets more matchable under illumination changes, whilst still discriminating between different people. Tests are conducted on the appearance of individuals from two camera views with greatly differing illumination conditions and invariance is evaluated through a similarity measure based upon colour histograms. In general, our results indicate that these techniques improve colour invariance; amongst them, full and controlled equalisation consistently showed the best performance. © Springer-Verlag Berlin Heidelberg 2007.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Comparison of techniques for mitigating the effects of illumination variations on the appearance of human targets	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	4842 LNCS	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Lake Tahoe, USA	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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	4842 LNCS	en_US

Abstract:

Several techniques have been proposed to date to build colour invariants between camera views with varying illumination conditions. In this paper, we propose to improve colour invariance by using data-dependent techniques. To this aim, we compare the effectiveness of histogram stretching, illumination filtration, full histogram equalisation and controlled histogram equalisation in a video surveillance domain. All such techniques have limited computational requirements and are therefore suitable for real time implementation. Controlled histogram equalisation is a modified histogram equalisation operating under the influence of a control parameter [1]. Our empirical comparison looks at the ability of these techniques to make the global colour appearance of single human targets more matchable under illumination changes, whilst still discriminating between different people. Tests are conducted on the appearance of individuals from two camera views with greatly differing illumination conditions and invariance is evaluated through a similarity measure based upon colour histograms. In general, our results indicate that these techniques improve colour invariance; amongst them, full and controlled equalisation consistently showed the best performance. © Springer-Verlag Berlin Heidelberg 2007.

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http://hdl.handle.net/10453/2725