A Local Scale Selection Scheme for Multiscale Area Integral Invariants

Wang, B; Gao, Y; Sun, C; Blumenstein, M; La Salle, J

A Local Scale Selection Scheme for Multiscale Area Integral Invariants

Wang, B Gao, Y Sun, C Blumenstein, M

La Salle, J

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Publication Type:: Conference Proceeding
Citation:: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016
Issue Date:: 2016-12-22

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Field	Value	Language
dc.contributor.author	Wang, B	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Sun, C	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	La Salle, J	en_US
dc.date.issued	2016-12-22	en_US
dc.identifier.citation	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016	en_US
dc.identifier.isbn	9781509028962	en_US
dc.identifier.uri	http://hdl.handle.net/10453/95555
dc.description.abstract	© 2016 IEEE. Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this paper, we propose a local scale selection scheme for generating multiscale area integral invariants. For the same scale level, the disc kernel size is not fixed and varies with the contour point where the disc is centered. This scheme also provides a scale assignment for emphasising the features extraction at finer scales. The strong discriminative power of the multiscale area integral invariant derived from the proposed scale selection scheme has been validated through experiments on very challenging leaf image retrievals.	en_US
dc.relation.ispartof	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016	en_US
dc.relation.isbasedon	10.1109/DICTA.2016.7796989	en_US
dc.title	A Local Scale Selection Scheme for Multiscale Area Integral Invariants	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this paper, we propose a local scale selection scheme for generating multiscale area integral invariants. For the same scale level, the disc kernel size is not fixed and varies with the contour point where the disc is centered. This scheme also provides a scale assignment for emphasising the features extraction at finer scales. The strong discriminative power of the multiscale area integral invariant derived from the proposed scale selection scheme has been validated through experiments on very challenging leaf image retrievals.

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http://hdl.handle.net/10453/95555