Improvement of quality characteristics of images based on the sliding window method

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Lyudmila Varlamova
Khamrokhon Tursunov

Abstract

This article discusses some window methods of image processing, such as Parzen, Rosenblatt, Kaiser, Epanechnikov windows. Using these methods, we can achieve the elimination of noise present in the images.


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How to Cite
Varlamova, L., & Tursunov, K. (2023). Improvement of quality characteristics of images based on the sliding window method. Scientific Collection «InterConf», (141), 324–330. Retrieved from https://archive.interconf.center/index.php/conference-proceeding/article/view/2316

References

Duda R., Hart P. Pattern recognition and scene analysis. Translation from English by G. G. Vayshteinv and A. M. Vaskovsky, edited by

V. L. Stefanyuk, MIR Publishing House, Moscow, 1976, - 509 p.

Gonzalez R., Woods R. Digital image processing. // Per. from English - Moscow. - Technosphere. - 2006. -1072 p.

Vapnik V.N., Chervonenkis A.Ya. Theory of Pattern Recognition (Statistical Problems of Learning). Publishing house "Nauka", Main

edition of physical and mathematical literature, Moscow, 1974, -416p.

Mokeev V.V., Tomilov S.V. On solving the problem of a small sample size when using linear discriminant analysis in face recognition

problems // Business Informatics. - 2013. No. 1 (23). pp. 37-43.

Lapko A.V., Chentsov S.V., Lapko V.A. Nonparametric models of pattern recognition in conditions of small samples // Avtometriya. -1999. - #6. pp. 105-113.

Voronin, V.V. Methods and algorithms for image restoration under conditions of incomplete a priori information: monograph / V.V. Voronin, V.I. Marchuk. - Shakhty: GOU VPO "YURGUES", 2010. -89s.

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, MaS, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Li FF. Imagenet large scale visual recognition challenge. International Journal of Computer Vision 2015; 115(3): 211-252. DOI: 10.1007/s11263-015-0816-y.

Varlamova L.P. Non-parametric classification methods in image recognition./ Journal of Xi'an University of Architecture & Technology: http://xajzkjdx.cn/Vol-11-Issue-12-2019/pp. 1494-1498. Issn No : 1006-7930. Volume XI, Issue XII, 2019, pp. 1494-1498. DOI:20.19001.JAT.2020.XI.I12.20.1891

Dvorkovich V.P., Dvorkovich A.V. Window functions for harmonic analysis of signals. –M: Technosphere. 2016, -216s.

10.Fairchild, M. D. (2013). Color Appearance Models (Vol. Third edition). Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db =edsebk&AN=594640

Hartley, R., & Zisserman, A. (2015). Multiple View Geometry in Computer Vision (2nd ed). Australia, Australia/Oceania: Cambridge University Press. Retrieved from http://search.ebscohost.com/login. aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1FEA5378