文献类型：期刊文献

英文题名：Combined invariants to blur and rotation using Zernike moment descriptors

作者：Zhu, Hongqing[1];Liu, Min[1];Ji, Hanjie[1];Li, Yu[1]

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

年份：2010

卷号：13

期号：3

起止页码：309

外文期刊名：PATTERN ANALYSIS AND APPLICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000280711200006)】；

语种：英文

外文关键词：Zernike moments; Radial moments; Blur invariants; Rotation invariants; Classification; Pattern recognition

摘要：Moment invariants that are not affected by geometric transform have been utilized as pattern features in a number of applications. But in most cases, images are processed subject to blur degradations. The traditional blur invariant sets were constructed using geometric moments, central moments or complex moments. However, these non-orthogonal moments are generally considered as a disadvantage over orthogonal moments, such as Zernike, pseudo-Zemike, and Legendre moments, in decreasing information redundancy and sensitivity to noises. To solve this problem, this paper addresses a method for recognizing objects in an image in a way that is invariant to images' blur and rotation transformations to improve the robustness to noises. The proposed method is based on Zernike descriptors which are orthogonal over a unit circle, and is invariant to a central symmetric blur, such as linear motion or out-of-focus blur. We present a mathematical framework of obtaining the Zernike moments of blurred images, and a framework of deriving the combined blur and rotation invariants. The classification experimental results are presented to confirm the proposed method outperforms other similar ones in the presence of various blur-degraded and rotation-transformed images.