A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.220035

Keywords:

air object recognition, contour analysis, Fourier descriptors, complex-valued neural network

Abstract

This paper reports a study into the methods for recognizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of categorizing multidimensional complex vectors of objects’ features based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordinates of its two-dimensional contour. A technique has been proposed to train a neural network to recognize the type of an air object based on three image classes corresponding to three projections. This makes it easier to solve the classification problem owing to a more compact arrangement of the multidimensional feature vectors. The architecture of an air situation video monitoring system has been suggested, which includes an image preprocessing module and a module of a complex-valued neural network. Pre-processing makes it possible to identify an object’s contour and build a sequence of normalized descriptors, which are partially independent of the spatial position of the object and the contour processing technique. Existing methods of air object recognition require significant computational resources and do not take into consideration the specificity of recognizing objects with three degrees of freedom or do not account for the complex nature of the numerical representation of a contour. This study has shown that the reported results make it easier to train a neural network and reduce the hardware requirements in order to solve the task of air situation video monitoring. The proposed solution leads to increased mobility and extends the scope of application of such systems, including individual devices

Author Biographies

Valentyn Yesilevskyi, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

PhD, Associate Professor

Department of Applied Mathematics

Andriy Tevyashev, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Doctor of Technical Sciences, Professor, Head of Department

Department of Applied Mathematics

Anton Koliadin, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Junior Researcher

Department of Applied Mathematics

References

  1. Strotov, V. V., Babyan, P. V., Smirnov, S. A. (2017). Aerial object recognition algorithm based on contour descriptor. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W4, 91–95. doi: https://doi.org/10.5194/isprs-archives-xlii-2-w4-91-2017
  2. Costa, L. da F., Cesar, Jr., R. M. (2018). Shape Classification and Analysis. CRC Press, 685. doi: https://doi.org/10.1201/9781315222325
  3. Hirose, A. (Ed.) (2013). Complex-Valued Neural Networks. Wiley. doi: https://doi.org/10.1002/9781118590072
  4. Sharma, N., Jain, V., Mishra, A. (2018). An Analysis Of Convolutional Neural Networks For Image Classification. Procedia Computer Science, 132, 377–384. doi: https://doi.org/10.1016/j.procs.2018.05.198
  5. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60 (6), 84–90. doi: https://doi.org/10.1145/3065386
  6. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D. et. al. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2015.7298594
  7. Mash, R., Becherer, N., Woolley, B., Pecarina, J. (2016). Toward aircraft recognition with convolutional neural networks. 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS). doi: https://doi.org/10.1109/naecon.2016.7856803
  8. Yesilevskyi, V., Teviashev, A., Koliadin, A. Transfer learning in aircraft classification. Available at: https://openarchive.nure.ua/bitstream/document/11942/1/3_IST.pdf
  9. Bradski, G., Kaehler, A. (2008). Learning OpenCV. Computer Vision with the OpenCV Library. O'Reilly Media, 555.
  10. Chaki, J., Dey, N. (2019). A Beginner’s Guide to Image Shape Feature Extraction Techniques. CRC Press, 152. doi: https://doi.org/10.1201/9780429287794
  11. Yang, M., Kpalma, K., Ronsin, J. (2012). Shape-Based Invariant Feature Extraction for Object Recognition. Intelligent Systems Reference Library, 255–314. doi: https://doi.org/10.1007/978-3-642-24693-7_9
  12. Rong, H.-J., Jia, Y.-X., Zhao, G.-S. (2014). Aircraft recognition using modular extreme learning machine. Neurocomputing, 128, 166–174. doi: https://doi.org/10.1016/j.neucom.2012.12.064
  13. Makarov, M. A., Berestneva, O. G., Andreev, S. Yu. (2014). Solving the problem of moving objects contour classification and recognition on video frame. Izvestiya Tomskogo politehnicheskogo universiteta, 325 (5), 77–83.
  14. Nguen, T. T. (2010). Algoritmicheskoe i programmnoe obespechenie dlya raspoznavaniya figur s pomoshch'yu Fur'e-deskriptorov i neyronnoy seti. Izvestiya Tomskogo politehnicheskogo universiteta, 317 (5), 122–125.
  15. Aizenberg, N. N., Ivaskiv, Yu. L., Pospelov, D. A. (1971). A certain generalization of threshold functions. Dokl. Akad. Nauk SSSR, 196 (6), 1287–1290. Available at: http://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=35992&option_lang=eng
  16. Guberman, N. (2016). On Complex Valued Convolutional Neural Networks. arXiv.org. Available at: https://arxiv.org/pdf/1602.09046.pdf
  17. Nitta, T. (2011). Ability of the 1-n-1 Complex-Valued Neural Network to Learn Transformations. Computational Modeling and Simulation of Intellect, 566–596. doi: https://doi.org/10.4018/978-1-60960-551-3.ch022
  18. Mönning, N., Manandhar, S. (2018). Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks. arXiv.org. Available at: https://arxiv.org/pdf/1811.12351.pdf
  19. Aizenberg, I. (2011). Complex-Valued Neural Networks with Multi-Valued Neurons. Springer. doi: https://doi.org/10.1007/978-3-642-20353-4
  20. Faijul Amin, M., Murase, K. (2009). Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing, 72 (4-6), 945–955. doi: https://doi.org/10.1016/j.neucom.2008.04.006
  21. Aizenberg, I., Moraga, C. (2006). Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm. Soft Computing, 11 (2), 169–183. doi: https://doi.org/10.1007/s00500-006-0075-5
  22. Granlund, G. H. (1972). Fourier Preprocessing for Hand Print Character Recognition. IEEE Transactions on Computers, C-21 (2), 195–201. doi: https://doi.org/10.1109/tc.1972.5008926

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Published

2020-12-31

How to Cite

Yesilevskyi, V., Tevyashev, A., & Koliadin, A. (2020). A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network. Eastern-European Journal of Enterprise Technologies, 6(4 (108), 48–57. https://doi.org/10.15587/1729-4061.2020.220035

Issue

Vol. 6 No. 4 (108) (2020): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2020 Valentyn Yesilevskyi, Andriy Tevyashev, Anton Koliadin

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