LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE
Leonid Timchenko
tumchenko_li@gsuite.duit.edu.uaState University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0001-5056-5913
Natalia Kokriatskaia
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0003-0090-3886
Volodymyr Tverdomed
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0002-0695-1304
Natalia Kalashnik
National Pirogov Memorial Medical University, Vinnytsia, Ukraine (Ukraine)
http://orcid.org/0000-0001-5312-3280
Iryna Shvarts
Vinnytsia National Technical University, Vinnytsia, Ukraine (Ukraine)
http://orcid.org/0000-0003-4344-5213
Vladyslav Plisenko
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0002-5970-2408
Dmytro Zhuk
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0001-8951-5542
Saule Kumargazhanova
D.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan (Kazakhstan)
http://orcid.org/0000-0002-6744-4023
Abstract
The article was aimed at studying the process of learning by the local difference threshold when filtering normal white noise. The existing learning algorithms for image processing were analyzed and their advantages and disadvantages were identified. The influence of normal white noise on the recognition process is considered. A method for organizing the learning process of the correlator with image preprocessing by the GQP method has been developed. The dependence of the average value of readings of the rank CCF (RCCF) of GQPs of the reference and current images, representing realizations of normal white noise, on the probability of formation of readings of zero GQP is determined. Two versions of the learning algorithm according to the described learning method are proposed. A technique for determining the algorithm efficiency estimate is proposed.
Keywords:
training, local difference threshold, filtering normal white noiseReferences
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Authors
Leonid Timchenkotumchenko_li@gsuite.duit.edu.ua
State University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0001-5056-5913
Authors
Natalia KokriatskaiaState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0003-0090-3886
Authors
Volodymyr TverdomedState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0002-0695-1304
Authors
Natalia KalashnikNational Pirogov Memorial Medical University, Vinnytsia, Ukraine Ukraine
http://orcid.org/0000-0001-5312-3280
Authors
Iryna ShvartsVinnytsia National Technical University, Vinnytsia, Ukraine Ukraine
http://orcid.org/0000-0003-4344-5213
Authors
Vladyslav PlisenkoState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0002-5970-2408
Authors
Dmytro ZhukState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0001-8951-5542
Authors
Saule KumargazhanovaD.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan Kazakhstan
http://orcid.org/0000-0002-6744-4023
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