文献类型：期刊文献

英文题名：Image representation using separable two-dimensional continuous and discrete orthogonal moments

作者：Zhu, Hongqing[1]

机构：[1]E China Univ Sci & Technol, Dept Elect & Commun Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：45

期号：4

起止页码：1540

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20115114619924);WOS:【SCI-EXPANDED(收录号:WOS:000300459000026)】；

基金：The authors would like to thank the anonymous referees for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China under Grant no. 60975004.

语种：英文

外文关键词：Bivariate; Separable; Classical orthogonal polynomials; Discrete orthogonal moments; Continuous orthogonal moments; Local extraction; Tensor product

摘要：This paper addresses bivariate orthogonal polynomials, which are a tensor product of two different orthogonal polynomials in one variable. These bivariate orthogonal polynomials are used to define several new types of continuous and discrete orthogonal moments. Some elementary properties of the proposed continuous Chebyshev-Gegenbauer moments (CGM). Gegenbauer-Legendre moments (GLM), and Chebyshev-Legendre moments (CLM), as well as the discrete Tchebichef-Krawtchouk moments (TKM), Tchebichef-Hahn moments (THM). Krawtchouk-Hahn moments (KHM) are presented. We also detail the application of the corresponding moments describing the noise-free and noisy images. Specifically, the local information of an image can be flexibly emphasized by adjusting parameters in bivariate orthogonal polynomials. The global extraction capability is also demonstrated by reconstructing an image using these bivariate polynomials as the kernels for a reversible image transform. Comparisons with the known moments are performed, and the results show that the proposed moments are useful in the field of image analysis. Furthermore, the study investigates invariant pattern recognition using the proposed three moment invariants that are independent of rotation, scale and translation, and an example is given of using the proposed moment invariants as pattern features for a texture classification application. (C) 2011 Elsevier Ltd. All rights reserved.