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Shape Classification using Multiscale Fourier-based Description in 2-D Space

Record type: Conference or Workshop Item (Other)

In shape recognition, the boundary and exterior parts are amongst the most discriminative features. In this paper, we propose new multiscale Fourier-based object descriptors in 2-D space, which represents the boundary and exterior parts of an object more than the central part. This representation is based on using a high-pass Gaussian filter at different scales. The proposed algorithm makes descriptors size, translation and rotation invariant as well as increasing discriminative power and immunity to noise. In comparison, the new algorithm performs better than elliptic Fourier descriptors and Zernike moments with respect to increasing noise.

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Citation

Direkoglu, Cem and Nixon, Mark S. (2008) Shape Classification using Multiscale Fourier-based Description in 2-D Space At IEEE International Conference on Signal Processing (ICSP 2008), China.

More information

Published date: 2008
Additional Information: Event Dates: October 2008
Venue - Dates: IEEE International Conference on Signal Processing (ICSP 2008), China, 2008-10-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 266817
URI: http://eprints.soton.ac.uk/id/eprint/266817
PURE UUID: 869fea50-a383-4378-a8ce-1c29b158ec73

Catalogue record

Date deposited: 21 Oct 2008 14:48
Last modified: 18 Jul 2017 07:11

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Contributors

Author: Cem Direkoglu
Author: Mark S. Nixon

University divisions


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