6 October 1998 Comparison study of geometric and orthogonal moments
Author Affiliations +
Proceedings Volume 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision; (1998); doi: 10.1117/12.325779
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
Abstract
Moments are widely used in pattern recognition, image processing and computer vision. To clarify and to guide the use of different types of moments, we present, in this paper, a comparison study of the behavior of different moments. After a brief introduction to geometric, Legendre, Hermite and Gaussian-Hermite moments, we analyze at first their behavior in spatial domain. Our analysis shows orthogonal moment base functions of different orders have different number of zero crossings and very different shapes, therefore they can better separate image features based in different nodes, which is very interesting for pattern analysis and shape classification. Moreover, Gaussian-Hermite moment base functions are much more smoothed, they are thus less sensitive to noise and avoid the artifacts introduced by window function discontinuity. We then analyze the spectral behavior of moments in frequency domain. Theoretical and numerical analyses show that Legendre and Gaussian-Hermite moments of different orders separate different frequency bands more effectively. It is also shown that Gaussian-Hermite moments give an approach to construct orthogonal features from wavelet analysis results. The orthogonality equivalence theorem is also presented. Our analysis is confirmed by numerical results, which are reported also.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jun Shen, Wei Shen, "Comparison study of geometric and orthogonal moments", Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325779; https://doi.org/10.1117/12.325779
PROCEEDINGS
12 PAGES


SHARE
KEYWORDS
Fourier transforms

Image processing

Wavelets

Image analysis

Image classification

Pattern recognition

Shape analysis

RELATED CONTENT

Pattern Recognition With A Spiral Sampling Technique
Proceedings of SPIE (October 13 1987)
Analysis of moment performance
Proceedings of SPIE (September 25 1998)
Shape recovery from a blurred image using wavelet analysis
Proceedings of SPIE (September 30 1999)
Shape Description With An Associative Memory
Proceedings of SPIE (October 15 1986)

Back to Top