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Pattern Recognition and Image Analysis

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01-07-2019 | REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES

The Stability and Noise Tolerance of Cartesian Zernike Moments Invariants

Authors: Yanjun Zhao, Saeid Belkasim, Alberto Arteta, Sanghoon Lee

Published in: Pattern Recognition and Image Analysis | Issue 3/2019

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Abstract

In real applications, it is quite common that shapes may have changes in orientation, scale, and viewpoint; a shape retrieval method should be unaffected by translation, rotation, and scaling. Zernike moments are widely used in shape retrieval, due to its rotation invariance. However, Zernike moments are not directly invariant under scaling and translation. Recently, Cartesian Zernike Moments Invariants (CZMI) were introduced to make Zernike moments directly invariant under scaling and translation. Although CZMI reduce the scale errors considerably, they are inconsistent and the scale errors increase for high aspect ratio shapes. In this paper, we introduce a scale invariance parameter which reduces the scale errors, improves the stability of the scale invariance and is more robust for wide range of shapes; even if the shapes are corrupted by different kinds of noises, such as Gaussian, Salt & Pepper and Speckle noise, our combined scale invariance parameter still has good performances.

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Title: The Stability and Noise Tolerance of Cartesian Zernike Moments Invariants
Authors: Yanjun Zhao
Saeid Belkasim
Alberto Arteta
Sanghoon Lee
Publication date: 01-07-2019
Publisher: Pleiades Publishing
Published in: Pattern Recognition and Image Analysis / Issue 3/2019
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661818040296

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