Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.105-112

Dynamic Scale Adaptation Algorithm of Image Etalon Functions

Mikhail Gavrikov, Roman Sinetsky

Abstract: An algorithm for large-scale adaptation of prototype functions representing image classes is proposed. The algorithm identifies the parameters of nonlinear scale distortions contained in the functions representing the observed image realizations, and then transforms the original prototype function using the previously proposed model. The algorithm works on a class of images of regular phase processes that have the property of quasi-similarity of shape. Two models of large-scale nonlinear transformations are considered: symmetric and asymmetric. The differences between the models and the practical results of their application are given. The algorithm was experimentally tested on the images of prototypes of fragments of speech signals, electrocardiosignals, and engine cylinder pressure detector signals. Examples and experimental data confirming the effectiveness of the algorithm are given. Conclusions are formulated about the possibility of using the algorithm with both models in practical problems.

Keywords: Image Recognition, Pattern Recognition, Prototype Function, Scale-Adapted Function

DOI: 10.25673/76940

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  1. M. M. Gavrikov, “Structural approximation and recognition of one-dimensional time images”, Concept and applications. Russian Electromechanics, 2003, vol 6, pp. 52-60.
  2. U. Grenander, “Lectures in Pattern Theory”, Volume I-III, Springer, 1976-1981, Berlin.
  3. R. van der Vlist, C. Taal, and R. Heusdens, “Tracking Recurring Patterns in Time Series Using Dynamic Time Warping”, 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, 2019, pp. 1-5, doi: 10.23919/EUSIPCO.2019.8903102.
  4. M. M. Gavrikov and R. M. Sinetsky, “Algorithms for the simulation of signals and spectral functions with a pulsating scale distortions”, University news, North-Caucasian region, Technical sciences series, 2013, vol 3, pp. 3-9.
  5. M. M. Gavrikov and R. M. Sinetsky, “Algorithms for segmentation of structural time images and their application in speech signal processing”, University news, North-Caucasian region, Technical sciences series, 2010, vol 1, pp. 18-24.
  6. A. Stan, C. Valentini-Botinhao, B. Orza, and M. Giurgiu, “Blind speech segmentation using spectrogram image-based features and Mel cepstral coefficients”, 2016 IEEE Spoken Language Technology Workshop (SLT), San Diego, CA, 2016, pp. 597-602, doi: 10.1109/SLT.2016.7846324.
  7. L. S. Fainzilberg, “Information technologies for processing complex waveforms”, Theory and practice, Ukraine, Kiev: Naukova dumka, 2008, 336 p.
  8. A. Mazumdar and L. Wang, “Covering arbitrary point patterns”, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, 2012, pp. 2075-2080, doi: 10.1109/Allerton.2012.6483478.
  9. M. M. Gavrikov and R. M. Sinetsky, “Algorithmic and numerical implementation of the structural approximation method for speech pattern recognition”, Russian Electromechanics, 2007, vol 2, pp. 52-59.
  10. J. B. Allen, “Short-term spectral analysis, synthesis, and modification by discrete Fourier transform”, IEEE Trans. on Acoustics, Speech, Signal Processing, 1997, vol. ASSP-25. N 3, pp. 235-238.
  11. K. Vijayan and K. S. R. Murty, “Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 12, pp. 2371-2383, Dec. 2015, doi: 10.1109/TASLP.2015.2479045.
  12. L. V. Zlatoustova, R. K. Potapova, and V. N. Trunin-Donskoy, “General and applied phonetics”, Moscow, MSU, 1986, 304 p.
  13. L. S. Fainzilberg, “Heart functional state diagnosticusing pattern recognition of phase space ECG-images”, Proc. 6th European Congress on IntelligentTechniques and Soft Computing (EUFIT '98).Aachen (Germany), 1998, N B-27, pp. 1878-1882.
  14. M. Yochum, Ch. Renaud, and S. Jacquir, “Automaticdetection of P, QRS and T patterns in 12 leads ECGsignal based on CWT”, Biomedical Signal Processingand Control, Elsevier, 2016, ff10.1016/j.bspc.2015.10.011ff.ffhal-01328478
  15. C. Yang, N. D. Aranoff, P. Green, andN.Tavassolian, “Classification of Aortic StenosisUsing Time–Frequency Features From Chest Cardio-Mechanical Signals”, IEEE Transactions onBiomedical Engineering, vol. 67, no. 6, pp. 1672-1683, June 2020, doi: 10.1109/TBME.2019.2942741.
  16. Q. Wang, T. Sun, Z. Lyu, and D. Gao, “A Virtual In-Cylinder Pressure Sensor Based on EKF and Frequency-Amplitude-Modulation Fourier-SeriesMethod”, Sensors 2019, 19, 3122, [Online].



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