Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)2022310.22363/2312-9735-2018-26-4-331-342Research ArticleInfluence of Noise on the DTW Metric Value in Object Shape RecognitionGostevIvan M<p>Doctor of Technical Sciences, professor of Department of Information Systems and Digital Infrastructure Management of National Research University “Higher School of Economics”</p>igostev@hse.ruSevastianovLeonid A<p>Professor, Doctor of Physical and Mathematical Sciences, Professor of Department of Applied Probability and Informatics of Peoples’ Friendship University of Russia (RUDN University)</p>sevastianov-la@rudn.ruNational Research University “Higher School of Economics”Peoples’ Friendship University of Russia (RUDN University)1512201826433134221122018Copyright © 2018, Gostev I.M., Sevastianov L.A.2018<p>The paper sets out one of the methodologies on image processing and recognition of the form of graphic objects. In it, at the first stage preliminary processing of the image with the purpose of extracting of characteristic attributes of the form of objects is made. Contours of objects are used as such attributes. For transformation of 2D contours of objects to one-dimensional contour function ArcHeight method has been used. The algorithm for identification contour functions based on metrics DTW is developed. Definition of the identification function based on this method is introduced. Features of application of metrics DTW are stated at identification of the form of objects. Matrices of distances of combinations the sample-sample and the sample-not sample are presented. Results of calculations of metrics DTW on a plenty of real data are analyzed. It is shown, that the developed algorithm allows to identify the form of objects independently of their position and an angle of turn on the image. Influence of the noise imposed on the image of object, on value of the metrics is investigated. Theoretical and practical results of such dependence are received; it shows that in a wide range (up to the ratio a signal/noise 10 dB) value of the metrics practically does not change. The positive parties and lacks of the offered algorithm are noted at identification of the form of object.</p>image processingpattern recognitionmetricDTWnoisesDTWобработка изображенийраспознавание образовметрикишумы[Seul M., O’Gorman L., Sammon M. Practical Algorithms for Image Analysis. — Cambridge University Press, 2000. — 295 p.][Pratt W. K. Digital Image Processing (Fourth edition). — Wiley, 2007. — 807 p.][Duda R. O., Hart P. E. Pattern Classification and Scene Analysis. — Wiley, 1973.][Gonzalez R., Woods R. Digital Image Processing. — Addison-Wesley Publishing Company, Reading, 1992. — 191 p.][Marr D. Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information. — Published March 15th 1983 by W. H. Freeman, 1983. — 397 p.][Klette R. 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