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A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

Abstract

Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91, 2013) in skeletonizing images like hand-written words.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61873280, 61502535, 61672033 and 61672248), Key Research and Development Program of Shandong Province (2017GGX10147), Fundamental Research Funds for the Central Universities (16CX02006A, 18CX02152A), Talent introduction project of China University of Petroleum (YJ201601003), and research project TIN2016-81079-R (AEI/FEDER, Spain-EU) and grant 2016-T2/TIC-2024 from Talento-Comunidad de Madrid, project TIN2016-81079-R (MINECO AEI/FEDER, SpainEU) and and InGEMICS-CM project (B2017/BMD-3691, FSE/FEDER, Comunidad de Madrid-EU).

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Correspondence to Tao Song.

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Song, T., Pang, S., Hao, S. et al. A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights. Neural Process Lett 50, 1485–1502 (2019). https://doi.org/10.1007/s11063-018-9947-9

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Keywords

  • Membrane computing
  • Spiking neural P system
  • Image skeletonizing
  • Zhang–Suen algorithm