Novel invariant Zernike moments as a shape descriptor for machine vision

Danhua Cao; Shixiong Jiang; Yubin Wu; Song Zhu

doi:10.1117/12.2036877

19 December 2013 Novel invariant Zernike moments as a shape descriptor for machine vision

Danhua Cao, Shixiong Jiang, Yubin Wu, Song Zhu

Proceedings Volume 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 90450A (2013) https://doi.org/10.1117/12.2036877
Event: International Conference on Optical Instruments and Technology (OIT2013), 2013, Beijing, China

Abstract

We present a way to construct a complete set of scaling rotation and translation invariants extract directly from Zernike moments. Zernike moment can be constructed by Radial moment. In our method in order to construct invariant Zernike moment is to achieve invariant Radial moment which is component of Zernike moment. We use matrix form to denote relationship between Radial and Zernike moment, which makes derivation more comprehensible. The translation invariant Radial moment is first introduced, for it is most complicated part of all the three invariant. Rotation and scaling invariant Radial moment is achieved by normalizing the factor caused by rotation and scaling. The form of invariant radial moment is to combine three parts of invariant. Some experiment has done to test the performance of invariance. In this experiment we take an image library containing 23,329 files which are built by translation rotation and zoom in out of one origin Latin character image. Most of the value of standard deviation ratio by mean of proposed moments is nearly 1%. In addition, retrieval experiment is to test the discrimination ability. MPEG-7 CE shape1 - Part A library is taken in this experiment. The recall rate in part A1 is 96.6% and is 100% in part A2.

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Danhua Cao, Shixiong Jiang, Yubin Wu, and Song Zhu "Novel invariant Zernike moments as a shape descriptor for machine vision", Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90450A (19 December 2013); https://doi.org/10.1117/12.2036877

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KEYWORDS

Dysprosium

Shape analysis

Image retrieval

Machine vision

Zernike polynomials

Zoom lenses

Optoelectronics

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