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Combined kernel for fast GPU computation of Zernike moments

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Abstract

Zernike moments, as a representative orthogonal moment, have been widely applied in the fields of image processing and pattern recognition. The calculations are time-consuming due to the complexity of definition. Based on the GPU octant symmetry algorithm in our previous work, this paper presents a novel algorithm to increase the resource utilization by the combined kernel. Also, it optimizes radial polynomials of Zernike moments to reduce amount of calculations. The experimental results demonstrated that the proposed algorithm achieved overall computational performance improvement for any sized images. Moreover, there is no compromise in terms of precision compared to the typical accurate algorithm.

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Acknowledgements

This work was supported by Scientific and technological Development Project of Jilin Province, China (No. 20180201043GX). This work was supported by a Grant from the National Natural Science Foundation of China (No. 61631009). This work was supported by the national key research and development plan of 13th five-year (No. 2017YFB0404800). This work was supported by “the Fundamental Research Funds for the Central Universities” under the Grant (No. 2017TD-19).

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Authors and Affiliations

  1. College of Communication Engineering, Jilin University, Changchun, 130012, Jilin, China

    Zengjun Zhao, Xinkai Kuang, Yukuan Zhu, Yecheng Liang & Yubo Xuan

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  1. Zengjun Zhao
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Zhao, Z., Kuang, X., Zhu, Y. et al. Combined kernel for fast GPU computation of Zernike moments. J Real-Time Image Proc 18, 431–444 (2021). https://doi.org/10.1007/s11554-020-00979-8

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