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Research Overview on Statistical Image Analysis Conducted at Ulyanovsk State Technical University

  • SCIENTIFIC SCHOOL OF ULYANOVSK STATE TECHNICAL UNIVERSITY, ULYANOVSK, THE RUSSIAN FEDERATION
  • K.K. Vasilyev’s Scientific School
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

This paper presents a series of research findings on methods for representation, filtering, parameter estimation (including geometric deformation parameters), detection, and recognition of multidimensional images and their sequences, conducted over 40 years at the scientific school of Ulyanovsk State Technical University, founded by Professor Konstantin Konstantinovich Vasilyev.

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REFERENCES

  1. A. I. Alexanin, M. A. Morozov, and E. V. Fomin, “The problems of image superimposition with one-pixel accuracy,” Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli iz Kosmosa 16 (1), 9–16 (2019). https://doi.org/10.21046/2070-7401-2019-16-1-9-16

  2. T. W. Anderson, The Statistical Analysis of Time Series (John Wiley and Sons, 2019).

    Google Scholar 

  3. N. A. Andriyanov, V. E. Dementiev, and A. G. Tashlinskiy, “Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks,” Komp’yuternaya Opt. 46, 139–160 (2022). https://doi.org/10.18287/2412-6179-co-922

    Article  ADS  Google Scholar 

  4. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Comput. Vision Image Understanding 110, 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  5. L. S. Biktimirov and A. G. Tashlinsky, “Features of image combinations in conditions of intense interference,” REDS: Telecommun. Devices Syst. 3, 321–324 (2013).

    Google Scholar 

  6. L. Sh. Biktimirov and A. G. Tashlinskii, “The reliability of pattern-match searching for the fragment on image using set of pseudo-gradient procedures,” in CEUR Workshop Proceedings (2017), Vol. 1901, pp. 28–31.

    Google Scholar 

  7. R. Bellman, “Invariant imbedding and random walk,” Proc. Am. Math. Soc. 13, 251–254 (1962). https://doi.org/10.1090/s0002-9939-1962-0137163-8

    Article  MathSciNet  Google Scholar 

  8. J. O. Berger, Statistical Decision Theory and Bayesian Analysis (Springer, New York, 1984). https://doi.org/10.1007/978-1-4757-4286-2

    Book  Google Scholar 

  9. G. E. Box and G. M. Jenkins, Time Series Analysis (Holden Day, San Francisco, 1970).

    Google Scholar 

  10. W. E. H. Chehade and P. Rogelj, “Comparison of mutual information and its point similarity implementation for image registration,” Int. J. Electr. Comput. Eng. 11, 2613–2620 (2021). https://doi.org/10.11591/ijece.v11i3.pp2613-2620

    Article  Google Scholar 

  11. J. Cui, V. S. Sheng, P. Zhao, D. Su, and S. Gong, “A comparative study of SIFT and its variants,” Meas. Sci. Rev. 13 (3), 122–131 (2013). https://doi.org/10.2478/msr-2013-0021

    Article  Google Scholar 

  12. N. Cvejic, C. N. Canagarajah, and D. R. Bull, “Image fusion metric based on mutual information and Tsallis entropy,” Electron. Lett. 42, 626–627 (2006). https://doi.org/10.1049/el:20060693

    Article  ADS  Google Scholar 

  13. V. E. Dementiev, R. G. Magdeev, and A. G. Tashlinskii, “Detecting anomalies in temporal image sequences based on object identification by the stochastic gradient adaptation,” in 2021 Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, 2021 (IEEE, 2021), pp. 1–5. https://doi.org/10.1109/itnt52450.2021.9649175

  14. V. E. Dementiev, R. G. Magdeev, and A. G. Tashlinskii, “Detection and identification of objects on multispectral satellite images,” J. Phys.: Conf. Ser. 1368, 032005 (2019). https://doi.org/10.1088/1742-6596/1368/3/032005

  15. B. E. Dement’ev, G. L. Minkina, and A. N. Repin, “Using methods of image processing for describing and optimizing the network coverage of mobile connections,” Pattern Recognit. Image Anal. 19, 84–88 (2009). https://doi.org/10.1134/s1054661809010155

    Article  Google Scholar 

  16. V. E. Dementiev, and A. G. Tashlinskii, “The use of stochastic parameter identification in the separation of mixtures of correlated deep gaussian models,” in 2020 Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, 2020 (IEEE, 2020), p. 9253288. https://doi.org/10.1109/itnt49337.2020.9253288

  17. G. V. Dikarina and A. G. Tashchlinskii, “Using pseudogradient procedures for estimating the quantiles of random fields,” Radioelektronnaya Tekh., No. 1, 116–119 (2008).

  18. S. S. Dikshit, “A recursive Kalman window approach to image restoration,” IEEE Trans. Acoust., Speech, Signal Process. 30, 125–140 (1984). https://doi.org/10.1109/tassp.1982.1163862

    Article  Google Scholar 

  19. D. E. Dudgeon, Multidimensional Digital Signal Processing (Prentice Hall, 1994).

    Google Scholar 

  20. A. D. Fida, A. V. Gaidel, N. S. Demin, N. Yu. Ilyasova, and E. A. Zamytskiy, “Automated combination of optical coherence tomography images and fundus images,” Komp’yuternaya Opt. 45, 721–727 (2021). https://doi.org/10.18287/2412-6179-co-892

    Article  ADS  Google Scholar 

  21. V. N. Frolov, V. A. Tupikov, V. A. Pavlova, and V. A. Alexandrov, “Informational image fusion methods in multichannel optoelectronic systems,” Izv. Tul. Gos. Univ. Tekh. Nauki, No. 11-3, 95–104 (2016).

  22. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. (Pearson Education, 2017).

    Google Scholar 

  23. I. S. Gruzman, V. S. Kirichuk, V. P. Kosykh, G. I. Peretyagin, and A. A. Spektor, (Novosibirsk. Gos. Tekh. Univ., Novosibisrk, 2000).

  24. P. V. Gulyaev, “The use of reference marks for precise tip positioning in scanning probe microscopy,” Komp’yuternaya Opt. 44, 420–426 (2020). https://doi.org/10.18287/2412-6179-co-641

    Article  ADS  Google Scholar 

  25. A. Khabibi, “Two-dimensional Bayesian estimation of images,” TIIER 60 (7), 153–159 (1972).

    Google Scholar 

  26. E. J. Hannan, Multiple Time Series, A Wiley Publication in Applied Statistics (John Wiley and Sons, New York, 1970).

  27. A. K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, 1989).

    Google Scholar 

  28. J. Casti and R. Kalaba, Imbedding Methods in Applied Mathematics, Ed. by R. Kalaba, Applied Mathematics and Computation (Addison-Wesley, Reading, Mass., 1973).

  29. A. N. Kamaev and D. A. Karmanov, “Visual navigation of an autonomous underwater vehicle based on the global search of image correspondences,” Komp’yuternaya Opt. 42, 457–467 (2018). https://doi.org/10.18287/2412-6179-2018-42-3-457-467

    Article  ADS  Google Scholar 

  30. D. D. Klovskii and V. A. Soifer, Processing of Spatial-Temporal Signals (in Information Transmission Channels) (Svyaz’, Moscow, 1976).

    Google Scholar 

  31. V. N. Klyachkin, V. R. Krasheninnikov, and Yu. E. Kuvaiskova, Forecasting and Diagnostics of Functioning Stability of a Technical Object (Rusains, Moscow, 2020).

    Google Scholar 

  32. R. O. Kovalenko, P. V. Smirnov, R. M. Ibragimov, and A. G. Tashlinskii, “Deformation field estimate for image sequence by applying stochastic adaptation in the block method,” CEUR Workshop Proc. 2665, 145–148 (2020).

    Google Scholar 

  33. R. Kovalenko and A. Tashlinskii, “Optimization of the histogram intervals number which approximate brightness probability distributions in stochastic image alignment based on information similarity measures,” in 2022 24th Int. Conf. on Digital Signal Processing and its Applications (DSPA), Moscow, 2022 (IEEE, 2022), pp. 1–5. https://doi.org/10.1109/dspa53304.2022.9805456

  34. V. R. Krashennikov, Yu. E. Kuvaiskova, and A. Yu. Malenova, “Models of systems of quasiperiodic processes based on cylindrical and circular images,” Izv. Samar. Nauchn. Tsentra Ross. Akad. Nauk 23, 103–110 (2021). https://doi.org/10.37313/1990-5378-2021-23-1-103-110

    Article  Google Scholar 

  35. V. R. Krasheninnikov and A. D. Kadeev, “Algorithm for shear and rotation estimation in images based on fixed point method,” Izv. Samar. Nauchn. Tsentra Ross. Akad. Nauk 4, 931–934 (2013).

    Google Scholar 

  36. V. R. Krasheninnikov, A. D. Kadeev, and M. A. Potapov, “Image convergence by the fixed point method,” Naukoemkie Tekhnol. 14 (4), 26–31 (2013).

    Google Scholar 

  37. V. R. Krasheninnikov, R. R. Mikeev, and M. V. Kuz’min, “Model and algorithm for imitation of megarelief of planets in form of image on the surface,” Radiotekhnika 174, 65–67 (2012).

    Google Scholar 

  38. V. R. Krasheninnikov, “Correlation analysis and synthesis of random field wave models,” Pattern Recognit. Image Anal. 25, 41–46 (2014). https://doi.org/10.1134/s1054661815010083

    Article  Google Scholar 

  39. V. R. Krasheninnikov, D. V. Kalinov, and Yu. G. Pankratov, “Spiral autoregressive model of a quasi-periodic signal,” Pattern Recognit. Image Anal. 11, 211–213 (2001).

    Google Scholar 

  40. V. R. Krasheninnikov and M. A. Potapov, “Estimation of parameters of geometric transformation of images by fixed-point method,” Pattern Recognit. Image Anal. 22, 303–317 (2012). https://doi.org/10.1134/s105466181202006x

    Article  Google Scholar 

  41. V. Krasheninnikov and K. Vasil’ev, “Multidimensional image models and processing,” in Computer Vision in Control Systems-3, Ed. by M. Favorskaya and L. Jain, Intelligent Systems Reference Library, Vol. 135 (Springer, Cham, 2018), pp. 11–64. https://doi.org/10.1007/978-3-319-67516-9_2

  42. V. R. Krasheninnikov, Yu. E. Kuvaiskova, O. E. Malenova, and A. Yu. Subbotin, “Testing hypotheses about covariance functions of cylindrical and circular images,” Pattern Recognit. Image Anal. 31, 431–442 (2021). https://doi.org/10.1134/s1054661821030159

    Article  Google Scholar 

  43. V. R. Krasheninnikov and Yu. E. Kuvaiskova, “Forecasting the dynamics of an object using autoregression models on the cylinder,” Radiotekhnika, No. 9, 36–39 (2016).

  44. V. R. Krasheninnikov, O. E. Malenova, A. U. Subbotin, L. I. Trubnikova, and M. L. Albutova, “Models of images of human biological fluids facies,” Procedia Comput. Sci. 192, 4055–4062 (2021). https://doi.org/10.1016/j.procs.2021.09.180

    Article  Google Scholar 

  45. V. Krasheninnikov, L. Trubnikova, A. Yashina, M. Albutova, and O. Malenova, “Algorithms for markers detection on facies images of human biological fluids in medical diagnostics,” in Computer Vision in Control Systems-6, Ed. by M. Favorskaya and L. Jain, Intelligent Systems Reference Library, Vol. 182 (Springer, Cham, 2020), pp. 107–125. https://doi.org/10.1007/978-3-030-39177-5_9

  46. R. Magdeev, G. Safina, and A. Tashlinskii, “Comparative analysis of objective quality functions for the stochastic gradient identification method,” in 2021 Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, 2021 (IEEE, 2021), pp. 1–4. https://doi.org/10.1109/itnt52450.2021.9649414

  47. R. G. Magdeev, M. N. Suetin, and A. G. Tashlinskii, “The effect of image pre-processing on objects adaptive stochastic identification efficiency,” CEUR Workshop Proc. 2665, 85–88 (2020).

    Google Scholar 

  48. R. G. Magdeev and A. L. Tashlinskii, “Efficiency of object identification for binary images,” Komp’yuternaya Opt. 43, 277–281 (2019). https://doi.org/10.18287/2412-6179-2019-43-2-277-281

    Article  ADS  Google Scholar 

  49. R. G. Magdeev and A. G. Tashlinskii, “Estimating the microstructural parameters of perlite steel by metallographic images,” Radiotekhnika 6, 35–40 (2017).

    Google Scholar 

  50. R. G. Magdeev and A. G. Tashlinskii, “Improving the efficiency of the method of stochastic gradient identification of objects in binary and grayscale images using their pre-processing,” in 22th Int. Conf. on Digital Signal Processing and Its Applications, Moscow, 2020 (IEEE, 2020), pp. 1–4. https://doi.org/10.1109/DSPA48919.2020.9213272

  51. R. G. Magdeev and A. G. Tashlinskiy, “Method for identification of perlite-class steel microstructure parameters using metallographic images,” CEUR Workshop Proc. 1901, 169–175 (2017). https://doi.org/10.18287/1613-0073-2017-1901-169-175

    Article  Google Scholar 

  52. R. G. Magdeev and A. G. Tashlinskii, “Extraction of pearlite grains in metallographic images of low-carbon steel,” Radiotekhnika 6, 33–37 (2018).

    Google Scholar 

  53. H. Y. Mussa, J. B. O. Mitchell, and A. M. Afzal, “The Parzen window method: In terms of two vectors and one matrix,” Pattern Recognit. Lett. 63, 30–35 (2015). https://doi.org/10.1016/j.patrec.2015.06.002

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  54. V. A. Malyshev and R. A. Minlos, Gibbs Random Fields: Cluster Expansions (Nauka, Moscow, 1988; Springer, Dordrecht 1991). https://doi.org/10.1007/978-94-011-3708-9

  55. Methods of Computer Image Processing, Ed. by V. A. Soifer (Fizmatlit, Moscow, 2001).

    Google Scholar 

  56. A. Nan, T. Matthew, R. Uriel, and R. Nilanjan, “Differentiable mutual information and matrix ex-ponential for multi-resolution image registration,” Med. Imaging Deep Learn., 527–543 (2021).

  57. M. B. Nevel’son and R. Z. Khsa’minskii, Stochastic Approximation and Recurrent Estimation (Nauka, Moscow, 1972).

    Google Scholar 

  58. Picture Processing and Digital Filtering, Ed. by T. S. Huang (Springer, Berlin, 1975). https://doi.org/10.1007/978-3-662-41612-9

    Book  Google Scholar 

  59. B. T. Polyak and Ya. Z. Tsypkin, “Pseudogradient adaptation and training algorithms,” Autom. Remote Control 34, 377–397 (1973).

    MathSciNet  Google Scholar 

  60. V. G. Repin and G. P. Tartakovskii, Stochastic Analysis at a Priori Uncertainty and Adaptation of Information Systems (Sovetskoe Radio, Moscow, 1977).

    Google Scholar 

  61. Yu. A. Rozanov, Markov Random Fields (Nauka, Moscow, 1981).

    Google Scholar 

  62. G. L. Safina, A. G. Tashlinskii, and M. G. Tsarev, “Optimization of estimation of mutual information in two images,” Radiotekhnika 6, 9–13 (2018).

    Google Scholar 

  63. G. Safina, A. Tashlinskii, and M. Tsaryov, “Adaptation of the mathematical apparatus of the Markov chain theory for the probabilistic analysis of recurrent estimation of image inter-frame geometric deformations,” CEUR Workshop Proc. 2391, 103–108 (2019). https://doi.org/10.18287/1613-0073-2019-2391-103-108

    Article  Google Scholar 

  64. A. S. Shalygin and Yu. I. Palagin, Applied Methods of Statistical Modeling (Mashinostroenie, Leningrad, 1986).

    Google Scholar 

  65. C. E. Shannon and W. Weaver, The Mathematical Theory of Communication (University of Illinois Press, Urbana, Ill., 1963).

    Google Scholar 

  66. B. Uidrou and S. Stirnz, Adaptive Signal Processing (Radio i Svyaz’, Moscow, 1989).

    Google Scholar 

  67. A. G. Tashlinskii, “Pseudogradient estimation of digital images interframe geometrical deformations,” in Vision Systems: Segmentation & Pattern Recognition, 2007 (Vienna,), pp. 465–494.

  68. A. G. Tashlinskii, “Computational expenditure reduction in pseudo-gradient image parameter estimation,” in Computational Science—ICCS 2003, Ed. by P. M. A. Sloot, D. Abramson, A. V. Bogdanov, Y. E. Gorbachev, J. J. Dongarra, and A. Y. Zomaya, Lecture Notes in Computer Science, Vol. 2658 (Springer, Berlin, 2003), pp. 456–462. https://doi.org/10.1007/3-540-44862-4_48

    Book  Google Scholar 

  69. A. G. Tashlinskii, Estimation of the Parameters of Spatial Sequence Deformation (Ul’yanovsk. Gos. Tekh. Univ., Ulyanovsk, 2000).

  70. A. G. Tashlinskii, “Optimization of goal function pseudogradient in the problem of interframe geometrical deformations estimation,” in Pattern Recognition Techniques, Technology and Applications, Ed. by P.‑Y. Yin (InTech, Rijeka, Croatia, 2008), pp. 249–280. https://doi.org/10.5772/6244

    Book  Google Scholar 

  71. A. G. Tashlinskii and R. G. Magdeev, “Improvement of reliability of object identification in binary images,” Inf.-Izmeritel’nye Upravlyayushchie Siste-My 12, 24–30 (2017).

    Google Scholar 

  72. A. G. Tashlinskii and R. G. Magdeev, “Estimating the parameters of pearlite-class steel microstructure by metallographic images,” Radiotekhnika 6, 35–40 (2017).

    Google Scholar 

  73. A. G. Tashlinskii and G. L. Safina, “The convergence rate optimization of geometrical image inter-frame transformations parameters at recurrent estimation,” J. Phys.: Conf. Ser. 1096, 012032 (2018). https://doi.org/10.1088/1742-6596/1096/1/012032

  74. A. G. Tashlinskii, G. L. Safina, and R. O. Kovalenko, “Optimal Euclidean distance of estimate mismatching at pseudogradient parameter estimation in interframe geometric deformations of images,” Inf.-Izmeritel’nye Upravlyayushchie Sist. 11, 33–39 (2018).

    Google Scholar 

  75. A. G. Tashlinskii, G. L. Safina, and R. O. Kovalenko, “Probabilistic finite modeling of stochastic estimation of image inter-frame geometric deformations,” J. Phys.: Conf. Ser. 1368, 032013 (2019). https://doi.org/10.1088/1742-6596/1368/3/032013

  76. A. G. Tashlinski, G. L. Safina, R. O. Kovalenko, and M. G. Tsarev, “Probabilistic finite modeling the stochastic estimation processes of interframe geometric image deformations,” Avtom. Protsessov Upr. 58 (4), 64–71 (2019). https://doi.org/10.35752/1991-2927-2019-4-58-64-71

    Article  Google Scholar 

  77. A. Tashlinskii, G. Safina, and M. Tsaryov, “Algorithm for different-time image alignment on the base of mutual information,” in 2022 24th Int. Conf. on Digital Signal Processing and Its Applications (DSPA), Moscow, 2022 (IEEE, 2022), pp. 1–6. https://doi.org/10.1109/dspa53304.2022.9790750

  78. A. G. Tashlinskii, G. L. Safina, M. G. Tsarev, and R. O. Kovalenko, “Using the Markov chains in error analysis of stochastic parameter estimation in interframe geometric image deformations,” Radiotekhnika 9, 62–67 (2019).

    Google Scholar 

  79. A. Tashlinskii and P. Smirnov, “Formation of inter-frame deformation field of images using reverse stochastic gradient estimation,” in Pattern Recognition-Selected Methods and Applications, Ed. by A. Zak (IntechOpen, Rijeka, Croatia, 2019), pp. 5–23. https://doi.org/10.5772/intechopen.83489

    Book  Google Scholar 

  80. A. Tashlinskiy, P. Smirnov, R. Kovalenko, and R. Ibragimov, “Application of stochastic adaptation in block method for estimating image sequence deformation field,” in 2020 22th Int. Conf. on Digital Signal Processing and its Applications (DSPA), Moscow (IEEE, 2020), pp. 1–4. https://doi.org/10.1109/dspa48919.2020.9213297

  81. A. G. Tashlinskii, P. V. Smirnov, and M. G. Tsaryov, “Pixel-by-pixel estimation of scene motion in video,” Int. Arch. Photogrammetry, Remote Sensing Spatial Inf. Sci. 42-2/W4, 61–65 (2017). https://doi.org/10.5194/isprs-archives-xlii-2-w4-61-2017

  82. A. G. Tashlinskii, R. O. Kovalenko, P. V. Smirnov, and M. N. Suetin, “Technique to model the movement of the scene using image sequence,” J. Phys.: Conf. Ser. 1368, 032012 (2019). https://doi.org/10.1088/1742-6596/1368/3/032012

  83. A. Tashlinskii, R. Kovalenko, and M. Tsaryov, “Efficiency of stochastic algorithm for different target functions in the task of estimating radio pulse time shift,” in 2020 Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, 2020 (IEEE, 2020), pp. 1–4. https://doi.org/10.1109/itnt49337.2020.9253261

  84. A. G. Tashlinskii, D. G. Kraus, and R. O. Kovalenko, “Probability prediction of estimations improvement at image parameters stochastic estimation,” J. Phys.: Conf. Ser. 1096, 012033 (2018). https://doi.org/10.1088/1742-6596/1096/1/012033

  85. A. G. Tashlinskii and M. G. Tsaryov, “Detection of radio pulses in unfiltered signals received by spatially distributed receivers,” Procedia Eng. 201, 296–301 (2017). https://doi.org/10.1016/j.proeng.2017.09.637

    Article  Google Scholar 

  86. A. G. Tashlinskii and S. V. Voronov, “Similarity and dissimilarity measures as objective function in image registration,” SWorld J. 11510, 32–48 (2015).

    Google Scholar 

  87. A. G. Tashlinskii, S. V. Voronov, and A. V. Zhukova, “Pseudogradient algorithm for estimating the parameters of image matching based on mutual information,” Radiotekhnika 6, 14–19 (2018).

    Google Scholar 

  88. A. G. Tashlinskiy and A. V. Zhukova, “Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences,” CEUR Workshop Proc. 1901, 235–239 (2017). https://doi.org/10.18287/1613-0073-2017-1901-235-239

    Article  Google Scholar 

  89. Ya. Z. Tsypkin, Information Theory of Identification (Fizmatlit, Moscow, 1995).

    Google Scholar 

  90. K. K. Vasilyev, “Statistical analysis of multidimensional images,” Pattern Recognit. Image Anal. 9, 732–748 (1999).

    Google Scholar 

  91. K. K. Vasil’ev, V. E. Dement’ev, and N. A. Andriyanov, “Doubly stochastic models of images,” Pattern Recognit. Image Anal. 25, 105–110 (2014). https://doi.org/10.1134/s1054661815010204

    Article  Google Scholar 

  92. K. K. Vasil’ev, V. E. Dement’ev, and N. A. Andriyanov, “Application of mixed models for solving the problem on restoring and estimating image parameters,” Pattern Recognit. Image Anal. 26, 240–247 (2016). https://doi.org/10.1134/s1054661816010284

    Article  Google Scholar 

  93. K. K. Vasil’ev, V. E. Dement’ev, and N. V. Luchkov, “Analysis of efficiency of detecting extended signals on multidimensional grids,” Pattern Recognit. Image Anal. 23, 1–9 (2012). https://doi.org/10.1134/s1054661812020198

    Article  Google Scholar 

  94. K. K. Vasil’ev, Signal Reception at Multiplicative Noise (Saratovsk. Tekh. Univ., Saratov, 1983).

    Google Scholar 

  95. K. K. Vasil’ev, “Bayesian differentiation and estimation of random sequences,” Radiotekh. Elektron. 30, 476–484 (1985).

    ADS  Google Scholar 

  96. K. K. Vasil’ev, “Signal detection in a sequence of images,” in Mathematical and Engineering Problems of Processing Visual Information (Vychislitel’nyi Tsentr Sib. Otd. Ross. Akad. Nauk, Novosibirsk, 1992), pp. 49–64.

    Google Scholar 

  97. A. A. Vasil’ev, “Autoregressions with multiple roots of characteristic equations,” Radiotekhnika, No. 11, 74–76 (2014).

  98. K. K. Vasil’ev and S. A. Ageev, “Application of adaptive decorrelation at image processing,” Naukoemkie Tekhnol. 3 (3), 4–24 (2002).

    Google Scholar 

  99. K. K. Vasil’ev and V. V. Balabanov, “Point signal detection on the background of disturbing images,” Radiotekhnika, No. 10, 86–89 (1991).

  100. A. A. Vasil’ev and V. E. Dement’ev, “Anomaly detection in multizone images,” Naukoemkie Tekhnol. 8 (9), 4–13 (2007).

    Google Scholar 

  101. A. V. Vasil’ev, V. E. Dement’ev, and N. V. Luchkov, “Detection of extended signals in multizone images,” Radiotekhnika, No. 9, 35–41 (2012).

  102. K. K. Vasil’ev and V. R. Krasheninnikov, Methods for Filtering Multidimensional Random Fields (Izd-vo Saratovsk. Univ., Saratov, 1990).

    Google Scholar 

  103. K. K. Vasil’ev and V. R. Krasheninnikov, “Adaptive anomaly detection algorithms in a sequence of multidimensional images,” Komp’yuternaya Opt. 14–15 (1), 125–132 (1994).

    Google Scholar 

  104. K. K. Vasil’ev, V. R. Krasheninnikov, I. N. Sinitsyn, and V. I. Sinitsyn, “Representation and rapid processing of multidimensional images,” Naukoemkie Tekhnol. 3, 4–24 (2002).

    Google Scholar 

  105. A. V. Vasil’ev and V. R. Krasheninnikov, Statistical Analysis of Image Sequences (Radiotekhnika, Moscow, 2017).

    Google Scholar 

  106. K. K. Vasil’ev, V. E. Dement’ev, and N. A. Andriyanov, “Estimating the parameters of doubly stochastic random fields,” Radiotekhnika, No. 7, 103–106 (2014).

  107. A. A. Vasil’ev, V. E. Dement’ev, and N. A. Andriyanov, “Analysis of estimation efficiency of varying parameters in a doubly stochastic model,” Radiotekhnika, No. 6, 12–14 (2014).

  108. Yu. V. Vizil’ter, S. Yu. Zheltov, A. V. Bondarenko, M. V. Osokov, and A. V. Morzhin, Image Processing and Analysis in Machine Vision Problems (Fizmatkniga, Moscow, 2010).

    Google Scholar 

  109. V. A. Vittikh, V. V. Sergeev, and V. A. Soifer, Image Processing in Automated Systems of Scientific Research (Nauka, Moscow, 1992).

    Google Scholar 

  110. M. P. Wachowiak, R. Smolikova, G. D. Tourassi, and A. S. Elmaghraby, “Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration,” Proc. SPIE 5032, 1090–1100 (2003). https://doi.org/10.1117/12.480867

    Article  ADS  Google Scholar 

  111. J. W. Woods, “Two-dimensional Kalman filtering,” in Two-Dimensional Digital Signal Processing I, Ed. by T. S. Huang, Topics in Applied Physics, Vol. 42 (Springer, Berlin, 1981), pp. 155–205. https://doi.org/10.1007/3-540-10348-1_19

    Book  Google Scholar 

  112. V. K. Zlobin, A. N. Kolesnikov, and B. V. Kostrov, “Correlation-extreme methods of combining aerospace images,” Vestn. Ryazansk. Gos. Radiotekh. Univ. 37 (3), 12–17 (2011).

    Google Scholar 

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Funding

The Russian Science Foundation provided financial support for this research under grant number 22-21-00513, available at https://rscf.ru/project/22-21-00513/.

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Authors and Affiliations

  1. Ulyanovsk State Technical University, 432027, Ulyanovsk, Russian Federation

    K. K. Vasilyev, V. R. Krasheninnikov & A. G. Tashlinskii

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  1. K. K. Vasilyev
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  2. V. R. Krasheninnikov
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  3. A. G. Tashlinskii
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Correspondence to K. K. Vasilyev, V. R. Krasheninnikov or A. G. Tashlinskii.

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Konstantin K. Vasilyev. Born July 1, 1948. Diploma radioengineer, 1972, Leningrad Electrotechnical Institute, Leningrad, USSR; Candidate of Technical Sciences, 1975, Leningrad Electrotechnical Institute, Leningrad, USSR. Dr. of Technical Sciences, 1985, Leningrad Electrotechnical Institute, Leningrad, USSR; Professor, 1987, Ulyanovsk State Technical University, Ulyanovsk, Russian Federation. Professor of the Department of Telecommunication at Ulyanovsk State Technical University, Ulyanovsk, Russian Federation.

The main results were obtained in statistical methods of signals and images processing: autoregressive models of images on multidimensional grids, special types of statistics of the decision rule for object detection, filtering and map** of signals and images. He is the author of 10 monographs and more than 500 scientific papers in peer reviewed journals and proceedings indexed in Web of Science, Scopus and Russian Science Citation, holder of 30 patents. Web of Science: 25 papers; 10 citations in 5 documents, Hirsh index is 2; SCOPUS: 3 papers, 56 citations in 35 documents, Hirsh index is 5; Russian Science Citation Index: 189 papers, 2100 citations, Hirsh index is 21. He was the head of several projects at the Russian Foundation for Basic Research.

He is the Chairman of the Ulyanovsk Regional Branch of the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Science.

Teaching practice: Ulyanovsk State Technical University, Ulyanovsk (professor), Russia. He was supervisor of 35 PhD students, 4 Dr. of Sciences and many graduate and master students.

Victor R. Krasheninnikov. Born May 27, 1945. Diploma mathematician (Computing Mathematics), 1967, Kazan State University, Kazan, USSR; Candidate of Technical Sciences (Mathematical Cybernetics), 1971, Kiev Institute of Cybernetics, Kiev, USSR. Dr. of Technical Sciences, 1995, Leningrad Electrotechnical Institute, Leningrad, USSR; Professor, 1996, Ulyanovsk State Technical University, Ulyanovsk, Russian Federation. Professor of the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University, Ulyanovsk, Russian Federation.

The main results were obtained in statistical methods of signals and images processing: wave and autoregressive models of images on rectangular and curvilinear grids, special types of statistics of the decision rule for object detection, pseudo-gradient algorithms for forecasting, filtering and map** of signals and images, recognition of speech signals by transforming them into images, analysis of images of bones and facies of human biological fluids for medical diagnostics. There are also papers on the theory of probabilistic automata and functions of fractional order. He is the author of 5 monographs and more than 350 scientific papers in peer reviewed journals and proceedings indexed in Web of Science, Scopus and Russian Science Citation, holder of 4 patents. Web of Science: 36 papers; 15 citations in 7 documents, Hirsh index is 3; SCOPUS: 37 papers, 86 citations in 49 documents, Hirsh index is 7; Russian Science Citation Index: 189 papers, 1363 citations, Hirsh index is 17. He was the head of several projects at the Russian Foundation for Basic Research.

He is the Scientific Secretary of the Ulyanovsk Regional Branch of the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Science.

Teaching practice: Ulyanovsk State Technical University, Ulyanovsk (professor), Russia and University of Nigeria in Nsukka, Nigeria (visiting senior lecturer). He was supervisor of 11 PhD students and many graduate and master students.

Alexander G. Tashlinskii. Born October 25, 1954. Diploma in Radio Engineering, 1977, Ulyanovsk Polytechnic Institute, Ulyanovsk, USSR; Candidate of Technical Sciences (Instruments and methods for measuring electrical and magnetic quantities), 1984, Ulyanovsk Polytechnic Institute, Ulyanovsk, USSR; Dr. of Technical Sciences (Application of Computer Science, Mathematical Modeling and Mathematical Methods in Scientific Research), 2000, Ulyanovsk State Technical University, Ulyanovsk, Russian Federation Professor; Professor, 2001, Ulyanovsk State Technical University, Ulyanovsk, Russian Federation. Head of the Department of Radio Engineering, Director of the Signal Research Center for Digital Image and Signal Processing, Ulyanovsk State Technical University, Ulyanovsk, Russian Federation.

The main area of scientific interests is statistical analysis and processing of sequences of digital images and signals, in particular, adaptive stochastic estimation of spatial deformations of images and video sequences. He is the author of 4 monographs and more than 300 scientific articles in peer-reviewed journals and collections indexed in Web of Science, Scopus and Russian Science Citation, the owner of 42 patents for inventions, about 20 registration certificates for computer programs. Web of Science: 18 papers; 17 citations in 11 documents, Hirsh index is 2; SCOPUS: 59 papers, 155 citations in 81 documents, Hirsh index is 7; Russian Science Citation Index: 288 papers, 1218 citations, Hirsh index is 15.

He was the head of 14 projects of the Russian Foundation for Basic Research, the Russian Science Foundation, and the Human Capital Foundation.

He is a member of the Ulyanovsk Regional Branch of the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Sciences.

Teaching practice: Ulyanovsk State Technical University, Ulyanovsk (Professor), Russia. He was supervisor of 11 PhD students and many graduate and master students.

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Vasilyev, K.K., Krasheninnikov, V.R. & Tashlinskii, A.G. Research Overview on Statistical Image Analysis Conducted at Ulyanovsk State Technical University. Pattern Recognit. Image Anal. 33, 1624–1656 (2023). https://doi.org/10.1134/S1054661823040508

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