{"id":1102,"date":"2021-01-15T21:38:46","date_gmt":"2021-01-15T19:38:46","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=1102"},"modified":"2021-03-23T12:17:31","modified_gmt":"2021-03-23T10:17:31","slug":"%d1%84%d0%b0%d1%81%d0%b5%d1%82%d0%ba%d0%be%d0%b2%d0%b8%d0%b9-%d0%bc%d0%b5%d1%82%d0%be%d0%b4-%d0%bf%d0%b5%d1%80%d0%b5%d1%82%d0%b2%d0%be%d1%80%d0%b5%d0%bd%d0%bd%d1%8f-%d0%b7%d0%be%d0%b1%d1%80%d0%b0","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=1102","title":{"rendered":"\u0424\u0430\u0441\u0435\u0442\u043a\u043e\u0432\u0438\u0439 \u043c\u0435\u0442\u043e\u0434 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043d\u0435\u0439\u0440\u043e\u043c\u0435\u0440\u0435\u0436\u0435\u0432\u043e\u0433\u043e \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f"},"content":{"rendered":"

\u0424\u0410\u0421\u0415\u0422\u041a\u041e\u0412\u0418\u0419 \u041c\u0415\u0422\u041e\u0414 \u041f\u0415\u0420\u0415\u0422\u0412\u041e\u0420\u0415\u041d\u041d\u042f \u0417\u041e\u0411\u0420\u0410\u0416\u0415\u041d\u042c \u0417\u0410 \u0414\u041e\u041f\u041e\u041c\u041e\u0413\u041e\u042e \u041d\u0415\u0419\u0420\u041e\u041c\u0415\u0420\u0415\u0416\u0415\u0412\u041e\u0413\u041e \u0420\u041e\u0417\u041f\u0406\u0417\u041d\u0410\u0412\u0410\u041d\u041d\u042f<\/p>\n

FACET METHOD OF IMAGE TRANSFORMATION BY MEANS OF NEURAL NETWORK RECOGNITION<\/p>\n

<\/a> \u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 147-153. \u041d\u043e\u043c\u0435\u0440: \u21161, 2020 (281)<\/strong>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u041e.\u0412. \u041c\u0410\u0417\u0423\u0420\u0415\u0426\u042c, \u0422.\u041a. \u0421\u041a\u0420\u0418\u041f\u041d\u0418\u041a, \u0410.\u0412. \u0406\u0417\u041e\u0422\u041e\u0412
\n\u0425\u043c\u0435\u043b\u044c\u043d\u0438\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\nO. MAZURETS, T.SKRYPNYK, A.IZOTOV
\nKhmelnytskyi National University
\nDOI:<\/strong> https:\/\/www.doi.org\/10.31891\/2307-5732-2020-281-1-147-153<\/a>
\n\u0420\u0435\u0446\u0435\u043d\u0437\u0456\u044f\/Peer review :<\/strong> 26. 01.2020 \u0440.
\n\u041d\u0430\u0434\u0440\u0443\u043a\u043e\u0432\u0430\u043d\u0430\/Printed :<\/strong> 14.02.2020 \u0440.<\/p>\n

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\u043f\u0456\u0434\u0432\u0438\u0449\u0438\u0442\u0438 \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u043f\u043e\u0434\u0430\u043b\u044c\u0448\u043e\u0433\u043e \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f. \u0422\u0430\u043a, \u0432 \u043f\u043e\u0440\u0456\u0432\u043d\u044f\u043d\u043d\u0456 \u0437 \u0443\u0441\u043f\u0456\u0448\u043d\u0456\u0441\u0442\u044e \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u043d\u0435\u043e\u0431\u0440\u043e\u0431\u043b\u0435\u043d\u0438\u0445 \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c, \u0434\u043b\u044f \u0437\u0430\u0448\u0443\u043c\u043b\u0435\u043d\u0438\u0445 \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u0437\u0440\u043e\u0441\u0442\u0430\u0454 \u0432 \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u043c\u0443 \u0437 36,17% \u0434\u043e 94,53%, \u0430 \u0434\u043b\u044f \u0431\u0435\u0437\u043a\u043e\u043d\u0442\u0443\u0440\u043d\u0438\u0445 \u0442\u0430 \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u043e\u0432\u0430\u043d\u0438\u0445 \u043e\u0431\u0440\u0430\u0437\u0456\u0432 \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0437\u0440\u043e\u0441\u0442\u0430\u0454 \u0432 \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u043c\u0443 \u0437 52,93% \u0434\u043e 88,16%.
\n\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u043d\u0435\u0439\u0440\u043e\u043c\u0435\u0440\u0435\u0436\u0430, \u0444\u0430\u0441\u0435\u0442\u043a\u0430, \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f.<\/p>\n

\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

The method of facet image conversion is a software resizing of the input image and is intended for use in the process of image recognition. Based on the developed facet method for image transformation, an application was created for neural network image recognition after processing by the developed method. To investigate the efficiency of the facet image conversion method, the results of the image recognition were compared before and after the facet image convolution. The developed facet image convolution information technology uses the facet image conversion method and allows the image to be recognized before scaling and after scaling using the perceptron neural network. There are two main components of information technology: facet image convolution and neural network image recognition. In the first stage, the image is processed by the facet method. First, an image dimension analysis is performed and a square is determined depending on the size of the input image, and then the dimension for the facet convolution is adjusted, if necessary. Necessity is determined by the ability to divide the image into squares. The next step is to set how noisy the image is, or vice versa. The next step is to recursively determine the affiliation of the pixel image to the original image, after which the intermediate material proceeds to the next stage of image recognition by the neural network. Researches have shown that the facet image conversion method allows to convert images in such a way as to increase the efficiency of further recognition. Thus, in comparison with the success of recognition of raw images, for noisy images the efficiency increases on average from 36.17% to 94.53%, and for the outline and segmented images the recognition efficiency increases on average from 52.93% to 88.16%.
\nKeywords:<\/strong> neural network, facet, recognition, image, transformation, determine, pixel.<\/p>\n

References<\/strong><\/p>\n

    \n
  1. GOROKHOVATSKY, O. (2018) Features of character image recognition using linear descriptions and result correction. Information processing systems, 4, p. 149-151.<\/li>\n
  2. OVCHARUK, O. & MAZURETS, O. (2019) Mathematical model of facet pre-recognition image transformation. Proceedings of the XI All-Ukrainian Scientific-Practical Conference “Actual Problems of Computer Sciences APKN-2019”, Vol. 1, p. 151-152.<\/li>\n
  3. PODOROZHNYAK, A., LYUBCHENKO, N. & LAGODA, O. (2015) The method of intellectual processing of multispectral images. Systems of information processing, 10. p. 123-125.<\/li>\n
  4. GOODFELOV, I., BENGIO, J. & COWERVILLE, A. (2016) Machine Learning. [Online] Available from: http:\/\/www.deeplearningbook.org [Accessed: 25 July 2019].<\/li>\n
  5. GAVRISH, B., TIMCHENKO, O., KULCHITSKY, R. & SEMENOVA, A. (2018) Features of construction of neural network image recognition systems. Modeling and information technologies, 83, p. 190-196.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u0424\u0410\u0421\u0415\u0422\u041a\u041e\u0412\u0418\u0419 \u041c\u0415\u0422\u041e\u0414 \u041f\u0415\u0420\u0415\u0422\u0412\u041e\u0420\u0415\u041d\u041d\u042f \u0417\u041e\u0411\u0420\u0410\u0416\u0415\u041d\u042c \u0417\u0410 \u0414\u041e\u041f\u041e\u041c\u041e\u0413\u041e\u042e \u041d\u0415\u0419\u0420\u041e\u041c\u0415\u0420\u0415\u0416\u0415\u0412\u041e\u0413\u041e \u0420\u041e\u0417\u041f\u0406\u0417\u041d\u0410\u0412\u0410\u041d\u041d\u042f FACET METHOD OF IMAGE TRANSFORMATION BY MEANS OF NEURAL NETWORK RECOGNITION \u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 147-153. \u041d\u043e\u043c\u0435\u0440: \u21161, 2020 (281) \u0410\u0432\u0442\u043e\u0440\u0438: \u041e.\u0412. \u041c\u0410\u0417\u0423\u0420\u0415\u0426\u042c, \u0422.\u041a. \u0421\u041a\u0420\u0418\u041f\u041d\u0418\u041a, \u0410.\u0412. \u0406\u0417\u041e\u0422\u041e\u0412 \u0425\u043c\u0435\u043b\u044c\u043d\u0438\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 O. MAZURETS, T.SKRYPNYK, A.IZOTOV Khmelnytskyi National University DOI: https:\/\/www.doi.org\/10.31891\/2307-5732-2020-281-1-147-153 \u0420\u0435\u0446\u0435\u043d\u0437\u0456\u044f\/Peer review : 26. 01.2020 \u0440. \u041d\u0430\u0434\u0440\u0443\u043a\u043e\u0432\u0430\u043d\u0430\/Printed : 14.02.2020 \u0440. \u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[13],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/1102"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1102"}],"version-history":[{"count":3,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/1102\/revisions"}],"predecessor-version":[{"id":5118,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/1102\/revisions\/5118"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1102"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1102"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1102"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}