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Shape classification via image-based multiscale description

Shape classification via image-based multiscale description
Shape classification via image-based multiscale description
We introduce a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF at different scales (standard deviation) represents the inner and central part of an object more than the boundary. On the other hand using the HPGF at different scales represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise. Multiscale description using HPGF in 2-D also outperforms Wavelet transform based multiscale contour Fourier descriptors and performs similar to the perimeter descriptors without any noise.
0031-3203
2134-2146
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Direkoglu, Cem and Nixon, Mark (2011) Shape classification via image-based multiscale description. Pattern Recognition, 44 (9), 2134-2146. (doi:10.1016/j.patcog.2011.02.016).

Record type: Article

Abstract

We introduce a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF at different scales (standard deviation) represents the inner and central part of an object more than the boundary. On the other hand using the HPGF at different scales represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise. Multiscale description using HPGF in 2-D also outperforms Wavelet transform based multiscale contour Fourier descriptors and performs similar to the perimeter descriptors without any noise.

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More information

Published date: 2011
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 272192
URI: http://eprints.soton.ac.uk/id/eprint/272192
ISSN: 0031-3203
PURE UUID: a956dd7b-59ee-4b0b-b726-eceb2702984a
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 15 Apr 2011 14:25
Last modified: 17 Dec 2019 02:04

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