Algorithms for Computing Zernike Moments and Image Reconstruction in Parallel Process

Deng, An-Wen; Wei, Chia-Hung; Gwo, Chih-Ying

doi:10.1109/icisce.2015.32

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Given its medium computational complexity, ADEA can be applied to real-time applications, such as reconstructing slowly time-varying signals. To further accelerate the running speed of ADEA, parallel implementation [17] or distributed clusters [23] could be employed.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

Bai

Zhao

Wang

et al. 2018

Information Sciences

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This paper aims at solving the sparse reconstruction (SR) problem via a multiobjective evolutionary algorithm. Existing multiobjective evolutionary algorithms for the SR problem have high computational complexity, especially in scenarios of high-dimensional reconstruction. Furthermore, these algorithms focus on estimating the whole Pareto front rather than the knee region, thus leading to solutions with limited diversity in the knee region and causing a waste of computational effort. To tackle these issues, this paper proposes an adaptive decomposition-based evolutionary approach (ADEA) for the SR problem. Firstly, we employ the decomposition-based evolutionary paradigm to guarantee a high computational efficiency and the diversity of solutions in the whole objective space. Then, we propose a two-stage iterative soft-thresholding (IST)-based local search operator for improving the convergence. Finally, we develop an adaptive decomposition-based environmental selection strategy, by which the decomposition in the knee region can be adjusted dynamically. This strategy makes it possible to focus the selection effort on the knee region, hence involving low computational complexity. Experimental results on images, and simulated and benchmark signals demonstrate the superiority of ADEA in terms of reconstruction accuracy and computational efficiency, compared to five stateof-the-art algorithms.

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Section: Discussionmentioning

confidence: 99%

“…into P t+1 (Line [16][17][18][19]. Once the number of selected solutions exceeds |V t |, the extra selected solutions from the last accepted level will be randomly removed (Line 20-22).…”

Section: Elitism Selectionmentioning

confidence: 99%

Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

Bai

Zhao

Wang

et al. 2018

Information Sciences

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show abstract

“…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. They implemented a parallelfriendly method for moment computation and image reconstruction based on Zernike moment.…”

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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A Brief Perusal of Image-based Diagnosis for COVID-19 Using Image Processing Perspective

Dandotiya,

Kumari

2024

Innovations in Sustainable Technologies and Computing

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Algorithms for Computing Zernike Moments and Image Reconstruction in Parallel Process

Cited by 5 publications

References 4 publications

Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

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

A Brief Perusal of Image-based Diagnosis for COVID-19 Using Image Processing Perspective

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