A parallel recurrence method for the fast computation of Zernike moments

Qin, Huafeng; Qin, L. C.; Xue, L.; Yu, Chang-shui

doi:10.1016/j.amc.2012.07.055

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…In addition, the Zernike orthogonal moments are invariant under rotation and the simplicity of image reconstruction. Due to these characteristics, the scientific interest in engineering is still increased [25].…”

Section: Image Transformation By Zernike Momentsmentioning

confidence: 99%

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Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

Liu

Huang

2015

Multimedia Systems

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Section: Image Transformation By Zernike Momentsmentioning

confidence: 99%

“…(25), α is used in coarse detection to detect the outliers. Figure 7 gives the test result with different α.…”

Section: Analyzing Detection Effect On Threshold Parameter Selectionmentioning

confidence: 99%

Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

Liu

Huang

2015

Multimedia Systems

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“…The emergence of new parallel architectures enriches the efforts toward this goal. Qin et al [26] proposed a parallel recurrence method to accelerate the implementation of the Zernike moment. In this context, Deng et al [27] extended the work of Qin and his colleague.…”

Section: Parallel Implementationmentioning

confidence: 99%

New machine learning method for image-based diagnosis of COVID-19

et al. 2020

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COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new MLmethod proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

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“…Since calculating ZMs has been sufficiently researched, the focus shifts to improving the efficiency for such calculations. Due to multiple factorial and trigonometric functions associated with Zernike basis polynomials, calculating ZMs is a complex and extensive process [7]. As higher-order ZMs are calculated, the computation time increases drastically, and the entire ZMs become unreliable as a result of numerical error accumulation.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, computing speed and numerical accuracy attract researchers when computing high-order ZMs as they emerge as two research issues. Different recurrence relation was proposed to compute either a single ZM or a whole set of ZMs for the purpose of improving calculation speed [7]. For numerical accuracy, the computation error of high-order ZMs based on q-recursive method was analyzed [8].…”

Section: Introductionmentioning

confidence: 99%

Blur Image Edge to Enhance Zernike Moments for Object Recognition

Gwo¹,

Deng²

2016

JCC

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Zernike moments (ZMs) are a set of orthogonal moments which have been successfully used in the fields of image processing and pattern recognition. A combination of edge blurring with ZMs computation was introduced. In this study, several kinds of artificial binary stripe images were used to investigate the effects of edge blurring on the absolute mean error of reconstructed image from high-order ZMs. After the blurring process, the reconstruction errors were increased dramatically at edge pixels, but decreased on non-edge pixels. The experimental results demonstrated that 2-pixel blurring approach provided better performance for reducing reconstruction error. Finally, a template matching between two real images was simulated to illustrate the effectiveness of the proposed method.

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A parallel recurrence method for the fast computation of Zernike moments

Cited by 9 publications

References 36 publications

Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis

New machine learning method for image-based diagnosis of COVID-19

Blur Image Edge to Enhance Zernike Moments for Object Recognition

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