Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Akerkar R, Sajja PS (2009) Bio-inspired computing: constituents and challenges. Int J Bio-inspired Comput 1(3):135–150

    Article  Google Scholar 

  2. Păun G, Rozenberg G, Salomaa A (2010) The Oxford handbook of membrane computing. Oxford University Press Inc, Oxford

    Book  MATH  Google Scholar 

  3. Ionescu M, Păun G, Yokomori T (2006) Spiking neural P systems. Fundam Inform 71(2):279–308

    MathSciNet  MATH  Google Scholar 

  4. Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659–1671

    Article  Google Scholar 

  5. Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  6. Chen H, Freund R, Ionescu M, Păun G, Pérez-Jiménez MJ (2007) On string languages generated by spiking neural P systems. Fundam Inform 75(1):141–162

    MathSciNet  MATH  Google Scholar 

  7. Zhang X, Liu Y, Luo B, Pan L (2014) Computational power of tissue P systems for generating control languages. Inf Sci 278:285–297

    Article  MathSciNet  MATH  Google Scholar 

  8. Păun A, Păun G (2007) Small universal spiking neural P systems. Biosystems 90(1):48–60

    Article  MATH  Google Scholar 

  9. Zhang G, Rong H, Neri F, Pérez-Jiménez MJ (2014) An optimization spiking neural p system for approximately solving combinatorial optimization problems. Int J Neural Syst 24(05):1440006

    Article  Google Scholar 

  10. Pan L, Păun G (2009) Spiking neural P systems with anti-spikes. Int J Comput Commun Control 4(3):273–282

    Article  Google Scholar 

  11. Metta VP, Krithivasan K, Garg D (2012) Computability of spiking neural P systems with anti-spikes. New Math Nat Comput 8(03):283–295

    Article  MathSciNet  MATH  Google Scholar 

  12. Song T, Pan L, Jiang K, Song B, Chen W (2013) Normal forms for some classes of sequential spiking neural P systems. IEEE Trans NanoBioscience 12(3):255–264

    Article  Google Scholar 

  13. Song T, Wang X (2015) Homogenous spiking neural p systems with inhibitory synapses. Neural Process Lett 42(1):199–214

    Article  Google Scholar 

  14. Zeng X, Zhang X, Pan L (2009) Homogeneous spiking neural P systems. Fundam Inf 97(1):275–294

    MathSciNet  MATH  Google Scholar 

  15. Song T, Wang X, Zhang Z, Chen Z (2014) Homogenous spiking neural P systems with anti-spikes. Neural Comput Appl 24:1833–1841

    Article  Google Scholar 

  16. Wang J, Hoogeboom HJ, Pan L, Paun G, Pérez-Jiménez MJ (2010) Spiking neural P systems with weights. Neural Comput 22(10):2615–2646

    Article  MathSciNet  MATH  Google Scholar 

  17. Ibarra OH, Păun A, Rodríguez-Patón A (2009) Sequential SNP systems based on min/max spike number. Theor Comput Sci 410(30):2982–2991

    Article  MathSciNet  MATH  Google Scholar 

  18. Zhang X, Luo B, Fang X, Pan L (2012) Sequential spiking neural P systems with exhaustive use of rules. Biosystems 108(1):52–62

    Article  Google Scholar 

  19. Ionescu M, Păun G, Yokomori T (2007) Spiking neural P systems with an exhaustive use of rules. Int J Unconv Comput 3(2):135–153

    Google Scholar 

  20. Zhang X, Wang B, Pan L (2014) Spiking neural P systems with a generalized use of rules. Neural Comput 26(12):2925–2943

    Article  MathSciNet  MATH  Google Scholar 

  21. Pan L, Păun G, Pérez-Jiménez MJ (2011) Spiking neural P systems with neuron division and budding. Sci China Inf Sci 54(8):1596–1607

    Article  MathSciNet  MATH  Google Scholar 

  22. Song T, Pan L (2014) Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy. IEEE Trans Nanobioscience 14(1):38–44

    Article  Google Scholar 

  23. Song T, Pan L (2015) Spiking neural P systems with rules on synapses working in maximum spikes consumption strategy. IEEE Trans NanoBioscience 14(1):38–44

    Article  Google Scholar 

  24. Song T, Pan L (2015) Spiking neural P systems with rules on synapses working in maximum spiking strategy. IEEE Trans Nanobioscience 14(4):465–477

    Article  Google Scholar 

  25. Cavaliere M, Ibarra OH, Păun G, Egecioglu O, Ionescu M, Woodworth S (2009) Asynchronous spiking neural P systems. Theor Comput Sci 410(24):2352–2364

    Article  MathSciNet  MATH  Google Scholar 

  26. Song T, Pan L, Păun G (2012) Asynchronous spiking neural P systems with local synchronization. Inf Sci 219:197–207

    Article  MathSciNet  MATH  Google Scholar 

  27. Păun G (2007) Spiking neural P systems with astrocyte-like control. J Univ Comput Sci 13(11):1707–1721

    MathSciNet  Google Scholar 

  28. Binder A, Freund R, Oswald M, Vock L (2007) Extended spiking neural p systems with excitatory and inhibitory astrocytes. In: Proceedings of fifth brainstorming week on membrane computing, pp 63–72

  29. Macías-Ramos LF, Pérez-Jiménez MJ (2012) Spiking neural P systems with functional astrocytes. In: Lecture notes in computer science, vol 7762, pp 228–242

  30. Song T, Gong F, Liu X, Zhao Y, Zhang X (2016) Spiking neural P systems with white hole neurons. IEEE Trans Nanobioscience 15(7):666–673

    Article  Google Scholar 

  31. Wang T, Zhang G, Zhao J, He Z, Wang J, Pérez-Jiménez MJ (2015) Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Trans Power Syst 30(3):1182–1194

    Article  Google Scholar 

  32. Metta VP, Krithivasan K, Garg D (2011) Modelling and analysis of spiking neural P systems with anti-spikes using pnet lab. Nano Commun Netw 2(2):141–149

    Article  Google Scholar 

  33. Ionescu M, Sburlan D (2007) Several applications of spiking neural P systems. In: 5th Proceedings of the workshop on membrane computing, Thessaloniki, pp 383–394

  34. Ionescu M, Sburlan D (2012) Some applications of spiking neural P systems. Comput Inform 27(3):515–528

    MathSciNet  MATH  Google Scholar 

  35. Song T, Zheng P, Wong MD, Wang X (2016) Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf Sci 372:380–391

    Article  Google Scholar 

  36. Adl A, Badr A, Farag I, Towards a spiking neural P systems OS, arXiv preprint arXiv:1012.0326

  37. Zeng X, Song T, Zhang X, Pan L (2012) Performing four basic arithmetic operations with spiking neural P systems. IEEE Trans Nanobioscience 11(4):366–374

    Article  Google Scholar 

  38. Ghosh-Dastidar S, Adeli H (2007) Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integr Comput Aided Eng 14(3):187–212

    Article  Google Scholar 

  39. Gupta A, Long LN (2007) Character recognition using spiking neural networks. In: 2007 International joint conference on neural networks, IEEE, pp 53–58

  40. Kang M, Palmer-Brown D (2008) A modal learning adaptive function neural network applied to handwritten digit recognition. Inf Sci 178(20):3802–3812

    Article  Google Scholar 

  41. Diazpernil D, Penacantillana F, Gutierreznaranjo MA (2013) A parallel algorithm for skeletonizing images by using spiking neural P systems. Neurocomputing 115:81–91

    Article  Google Scholar 

  42. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  43. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  44. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  45. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670

    Article  MathSciNet  MATH  Google Scholar 

  46. Hong C, Yu J, Tao D, Wang M (2015) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751

    Google Scholar 

  47. Hong C, Chen X, Wang X, Tang C (2016) Hypergraph regularized autoencoder for image-based 3D human pose recovery. Signal Process 124:132–140

    Article  Google Scholar 

  48. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  49. Yu Z, Yu J, Xiang C, Fan J, Tao D (2018) Beyond bilinear: generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans Neural Netw Learn Syst 99:1–13

    Google Scholar 

  50. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  51. Yu Z, Yu J, Fan J, Tao D (2017) Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: Proceedings of IEEE international on conference computer visualization, vol 3

  52. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  53. Mitkov R (2005) The Oxford handbook of computational linguistics. Oxford University Press, Oxford

    MATH  Google Scholar 

  54. Zhang T, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239

    Article  Google Scholar 

  55. Penacantillana DMF, Berciano A (2012) Parallel skeletonizing of digital images by using cellular automata. Comput Topol Image Context 115:81–91

    Google Scholar 

  56. Wang X, Song T, Gong F, Zheng P (2016) On the computational power of spiking neural P systems with self-organization. Sci Rep 6:27624

    Article  Google Scholar 

  57. Zhang X, Pan L, Paun A (2015) On the universality of axon P systems. IEEE Trans Neural Netw 26(11):2816–2829

    Article  MathSciNet  Google Scholar 

  58. Song T, Rodrłguez-Patn A, Zheng P, Zeng X (2017) Spiking neural P systems with colored spikes. IEEE Trans Cogn Dev Syst 99:1–1

    Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, 266580, Shandong, China

    Tao Song, Shanchen Pang & Shaohua Hao

  2. Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte, 28660, Madrid, Spain

    Tao Song & Alfonso Rodríguez-Patón

  3. Department of Accounting and Information Systems, University of Canterbury, Christchurch, 8041, New Zealand

    Pan Zheng

Authors
  1. Tao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shanchen Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaohua Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alfonso Rodríguez-Patón
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Song.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9947-9

Keywords

Search

Navigation