Paper
16 September 2002 Image invariant moments for shape description
Yunlong Sheng, Ziliang Ping, RiGeng Wu
Author Affiliations +
Abstract
We show that the transformation with radial polynomial and circular Fourier kernel of two-dimensional image can generate image moments, which are invariant to rotation, translation and scale changes. Among them the orthogonal Fourier-Mellin moments using the generalized Jacobi radial polynomials show better performance that the Zernike moments. We introduce new Chebyshev-Fourier moments using Chebyshev radial polynomials, which improve the behavior of the orthogonal Fourier-Mellin moments in regions close to the center of image. Experimental results are shown for the image description performance of the Chebyshev-Fourier moments in terms of image reconstruction errors and sensitivity to noise. In the cases of binary or contour shapes the Fourier-Mellin moments of single orders are able to describe and reconstruct the shapes.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunlong Sheng, Ziliang Ping, and RiGeng Wu "Image invariant moments for shape description", Proc. SPIE 4929, Optical Information Processing Technology, (16 September 2002); https://doi.org/10.1117/12.483220
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KEYWORDS
Binary data

Computing systems

Image restoration

Pattern recognition

Image segmentation

Image quality

Fourier transforms

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