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. O...

Full description

Saved in:

Bibliographic Details
Published in:	Pattern recognition 2011-09, Vol.44 (9), p.2134-2146
Main Authors:	Direkoğlu, Cem, Nixon, Mark S.
Format:	Article
Language:	eng
Subjects:	Applied sciences Artificial intelligence Boundaries Classification Computer science control theory systems Computer vision Detection, estimation, filtering, equalization, prediction Exact sciences and technology Exteriors Feature extraction Fourier analysis Fourier-based description Gaussian Gaussian filter Information, signal and communications theory Multiscale representation Noise Pattern recognition. Digital image processing. Computational geometry Shape classification Signal and communications theory Signal, noise Standard deviation Telecommunications and information theory Translations
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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. ► We introduce a new multiscale Fourier-based object description in 2-D space. ► A high-pass Gaussian filter (HPGF) is used for multiscale generation. ► A low-pass Gaussian filter (LPGF) is also used for multiscale generation. ► Using the HPGF performs better than using the LPGF in classification. ► Using the HPGF also outperforms the popular shape description techniques.
ISSN:	0031-3203 1873-5142