Skip to main content
SpringerLink
Log in

A Graphical Interface Learning Tool for Image Processing Through Analog CNN

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This work presents a cellular neural network (CNN) learning tool based on the Center of Mass Algorithm (CMA), which provides a friendly graphical user interface. As original contributions of this work, the learning tool here developed for CNN features a few adaptations in CMA to improve the training process for grayscale image filtering, such as decreasing learning rates and training with multiple image sets, and comprises many graphical user interface facilities for CNN designers. The training capability of the developed tool is validated through several filter applications, among which lowpass and highpass filters with finite or infinite impulse responses. A comparative analysis is performed between the theoretical responses of the filters and the results obtained from the simulation of a CMOS analog CNN configured by the parameters determined through the learning tool. The training process was very fast for the lowpass filters and acceptable RMS pixel errors have been obtained for all examples, confirming the reliability of the learning tool.

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

Access this article

Log in via an institution

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
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. A.A. Abdullah, B.S. Chize, Z. Zakaria, Design of cellular neural network (CNN) simulator based on Matlab for brain tumor detection. J. Med. Imag. Health Inform. 2(3), 1–11 (2012). https://doi.org/10.1166/jmihi.2012.1095

    Article  Google Scholar 

  2. F. S. de Andrade, Y. O. da G. Souza, E. P. Santana and A. I. A. Cunha, Image filtering in a CMOS analog CNN, In: Proceedings of the 2015 IEEE 6th Latin American Symposium on Circuits & Systems. LASCAS, (2015), pp. 1–4. https://doi.org/10.1109/LASCAS.2015.7250426

  3. F. S. Andrade, Filtragem de Imagens em Escala de Cinza por Meio de Rede Neuronal Celular Analógica em Tecnologia CMOS, M.S. thesis, Dept. of Electrical and Comput. Engineering, Univ. Federal da Bahia (UFBA), Salvador, BA, 2015.

  4. F. S. de Andrade, E. P. Santana, A. I. A. Cunha, E. F. de Simas Filho, G. C. Gonçalves and A. J. S. de Sousa, Learning for Image Processing: Center of Mass versus Genetic Algorithms,” In: Proceedings of the 2019 IEEE 10th Latin American Symposium on Circuits & Systems. LASCAS, (2019), pp. 41–44. https://doi.org/10.1109/LASCAS.2019.8667559

  5. A. Azamimi Abdullah, S. C. Ling and W. K. W. Ahmad, Design of an automated breast cancer masses detection in mammogram using cellular neural network (CNN) algorithm. Adv. Sci. Lett. 20(1), 254–258 (2014). https://doi.org/10.1166/asl.2014.5307

  6. A. Baştürk, E. Günay, Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm. Expert Syst. Appl. 36(2), 2645–2650 (2009). https://doi.org/10.1016/j.eswa.2008.01.082

    Article  Google Scholar 

  7. R. Carmona, F. Giménez-Garrido, R. Dominguez-Castro, S. Espejo, A. Rodríguez-Vázquez, CMOS realization of a 2-layer CNN universal machine chip. Int. J. Neural Syst. 13(6), 435–442 (2003). https://doi.org/10.1142/S0129065703001716

    Article  Google Scholar 

  8. R. Carmona-Galán, F. Giménez-Garrido, R. Dominguez-Castro, S. Espejo, T. Roska, C. Rekeczky, I. Petrás, A. Rodríguez-Vázquez, A bio-inspired two-layer mixed-signal flexible programmable chip for early vision. IEEE Trans. Neural Networks 14(5), 1313–1333 (2003). https://doi.org/10.1109/TNN.2003.816377

    Article  Google Scholar 

  9. L.O. Chua, L. Yang, Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35(10), 1257–1272 (1988). https://doi.org/10.1109/31.7600

    Article  MathSciNet  MATH  Google Scholar 

  10. L.O. Chua, T. Roska, Cellular neural networks and visual computing: foundations and applications (Cambridge University Press, Cambridge, 2002)

    Book  Google Scholar 

  11. S. Espejo, R. Carmona, R. Domínguez-Castro and A. Rodríguez-Vázquez, A VLSI-oriented continuous-time CNN model. Int. J. Circuit Theory Appl. 24(3), 341–356 (1996). https://doi.org/10.1002/(SICI)1097-007X(199605/06)24:3<341::AID-CTA920>3.0.CO;2-L

  12. R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital image processing Using MATLAB (Pearson Prentice Hall, 2003)

    Google Scholar 

  13. C. Guzelis and S. Karamahmut, Recurrent perceptron learning algorithm for completely stable cellular neural networks, In: Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications. CNNA-94, (1994), pp. 177–182. https://doi.org/10.1109/CNNA.1994.381688.

  14. S. Kim, C. Song, J. Jang, J. Paik, Edge-aware image filtering using a structure-guided CNN. IET Image Proc. 14(3), 472–479 (2020). https://doi.org/10.1049/iet-ipr.2018.6691

    Article  Google Scholar 

  15. T. Kozek, T. Roska and L. O. Chua, Genetic algorithm for CNN template learning. IEEE Trans. Circuits Syst. I Fund. Theory Appl. 40(6), 392–402 (1993). https://doi.org/10.1109/81.238343.

  16. J. Lai and P. C. Wu 2004 Architectural design and analysis of learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for nanoelectronic systems. IEEE Trans Very Large Scale Integration (VLSI) Syst. 12(11), 1182–1191 https://doi.org/10.1109/TVLSI.2004.836309

  17. B. Mirzai, Z. Cheng and G. S. Moschytz, Learning algorithms for cellular neural networks, In: Proceedings of the 1998 IEEE International Symposium on Circuits and Systems. ISCAS’98, vol. 3 (1998), pp. 159–162. https://doi.org/10.1109/ISCAS.1998.703935

  18. A. Rodríguez-Vázquez, S. Espejo, R. Domínguez-Castro, J. Huertas and E. Sanchez-Sinencio, Current-mode techniques for the implementation of continuous- and discrete-time cellular neural networks. IEEE Trans. Circuits Syst. II Analog Digital Signal Process., 40(3), 132–146 (1993). https://doi.org/10.1109/82.222812

  19. E. Saatci and V. Tavsanoglu, Fingerprint image enhancement using CNN Gabor-type filters, In: Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, (2002), pp. 377–382. https://doi.org/10.1109/CNNA.2002.1035073

  20. E. Santana, R. C. Freire and A. I. Cunha, (2012) A compact low-power CMOS analog FSR model-based CNN. J. Integr. Circuits Syst. 7(1) 72–80. https://doi.org/10.29292/jics.v7i1.357

  21. M. Tanaka, H. Aomori, K. Oshima, M. Hasler and Y. Nishio, Leaning theory of cellular neural networks based on covariance structural analysis, In: Proceedings of the 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications. CNNA 2010, (2010), pp. 1–4. https://doi.org/10.1109/CNNA.2010.5430326.

Download references

Acknowledgements

Authors would like to acknowledge Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES and Fundação de Amparo à Pesquisa do Estado da Bahia—FAPESB for the financial support.

Author information

Authors and Affiliations

  1. Departamento de Engenharia Elétrica – Escola Politécnica, Universidade Federal da Bahia – UFBA, Rua Aristides Novis 2, Federação, Salvador, BA, 40210-630, Brazil

    Fabian Souza de Andrade, Ygor Oliveira da Guarda Souza, Ygor Oliveira da Guarda Souza, Edson Pinto Santana & Ana Isabela Araújo Cunha

Authors
  1. Fabian Souza de Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ygor Oliveira da Guarda Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edson Pinto Santana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ana Isabela Araújo Cunha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Isabela Araújo Cunha.

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

de Andrade, F.S., Souza, Y.O.d., Santana, E.P. et al. A Graphical Interface Learning Tool for Image Processing Through Analog CNN. Circuits Syst Signal Process 41, 4952–4976 (2022). https://doi.org/10.1007/s00034-022-02013-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02013-7

Keywords

Search

Navigation