Work place: National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute', Kyiv, Ukraine
Research Interests: Big data and learning analytics, Data Structures and Algorithms, Multimedia Information System, Image and Sound Processing
Yevgeniya S. Sulema is Vice-Dean of the Faculty of Applied Mathematics, and Associated Professor of the Computer Systems Software Department at the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”. She received her Ph.D. degree from the National Technical University of Ukraine “Kyiv Polytechnic Institute” in 1999. She is member of editorial advisory board of Systemics, Cybernetics and Informatics journal and member of the program committees of several international conferences. She is member of the International Institute of Informatics and Systemics. Research in her laboratory MDP-RG covers image and audio processing, multimedia data protection methods, mulsemedia data representation.
DOI: https://doi.org/10.5815/ijmecs.2020.03.02, Pub. Date: 8 Jun. 2020
In this work we present two methods of vector graphic objects retrieval based on a fuzzy description of their shapes. Both methods enable the retrieval of vector images resembling to a given fuzzy pattern. The basic method offers a geometrical interpretation of a fuzziness measure as a radius of a circle with the center in each vertex of a given candidate object. It enables the representation of uncertain information about a pattern object defined by its “fuzzy” vertices. The advanced method generalizes this approach by considering an ellipse instead of a circle. The basic method can be used for the comparison of polygons and other primitives in vector images. The advanced method can be used for complex shapes retrieval. To enable saving a “fuzzy” image as a file, the modification of the SVG format with a new attribute “fuzziness” is proposed for both methods. The advanced method practical implementation is illustrated by the retrieval of medical images, namely, heart computer tomography images.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2018.05.02, Pub. Date: 8 May 2018
The paper presents the method of medical images similarity estimation based on feature extraction and analysis. The proposed method has been developed for and tested on rat brain histological images, however, it can be applied for other types of medical images, since the general approach is based on consideration of the shape of core components present in a given template image. The proposed method can be used in image analysis tools in a wide range of image-based medical investigations, in particular, in the brain researches.
The theoretical background of the proposed method is presented in the paper. The expert evaluation approach used for assessment of the proposed method effectiveness is explained and illustrated by examples. The method of medical images similarity estimation based on feature analysis consists of several stages: colour model conversion, image normalization, anti-noise filtering, contours search, conversion, and feature analysis. The results of the proposed method algorithmic realization are demonstrated and discussed.
DOI: https://doi.org/10.5815/ijisa.2017.08.04, Pub. Date: 8 Aug. 2017
The proposed method of graphical data protection is a combined crypto-steganographic method. It is based on a bit values transformation according to both a certain Boolean function and a specific scheme of correspondence between MSB and LSB. The scheme of correspondence is considered as a secret key. The proposed method should be used for protection of large amounts of secret graphical data.[...] Read more.
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