Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

Daradkeh Yousef Ibrahim; Tvoroshenko, I. S.; Gorokhovatskyi, V. O.; Latiff, L. A.; Ahmad N.

Публікація:
Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

dc.contributor.author	Daradkeh Yousef Ibrahim
dc.contributor.author	Tvoroshenko, I. S.
dc.contributor.author	Gorokhovatskyi, V. O.
dc.contributor.author	Latiff, L. A.
dc.contributor.author	Ahmad N.
dc.date.accessioned	2021-03-19T12:31:16Z
dc.date.available	2021-03-19T12:31:16Z
dc.date.issued	2021
dc.description.abstract	ABSTRACT The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problemof an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the in uence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classi cation and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The veri cation of theoretical foundations and evaluation of the effectiveness of the proposed data processingmethods for real image bases is performed using theOpenCV library. The applied signi cance of thework is substantiated according to the criterion of data processing timewithout reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.	uk_UA
dc.identifier.citation	Daradkeh, Y.I., Tvoroshenko, I., Gorokhovatskyi, V., Latiff, L.A., and Ahmad, N. (2021) Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic, IEEE Access, 9, pp. 13417-13428, DOI: 10.1109/ACCESS.2021.3051625	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/15013
dc.language.iso	en_US	uk_UA
dc.subject	computer vision	uk_UA
dc.subject	data granulation	uk_UA
dc.subject	fuzzy logic	uk_UA
dc.subject	uniqueness index	uk_UA
dc.title	Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication