Etienne Kerre

Work place: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium



Research Interests: Image Compression, Image Manipulation, Image Processing


Prof. Dr. Etienne E. Kerre obtained his M.Sc. degree in Mathematics in 1967 and his Ph.D. in Mathematics in 1970 from Ghent University. Since 1984, he has been a lector, and since 1991, a full professor at Ghent University. In 2010 he became a retired professor. He is a referee for more than 80 international scientific journals, and also a member of the editorial board of international journals and conferences on fuzzy set theory. He was an honorary chairman at various international conferences. In 1976, he founded the Fuzziness and Uncertainty Modeling Research Unit (FUM) and since then his research has been focused on the modeling of fuzziness and uncertainty, and has resulted in a great number of contributions in fuzzy set theory and its various generalizations. Especially the theories of fuzzy relational calculus and of fuzzy mathematical structures owe a very great deal of him. Over the years he has also been a promotor of 30 Ph.D's on fuzzy set theory. His current research interests include fuzzy and intuitionistic fuzzy relations, fuzzy topology, and fuzzy image processing. He has authored or co-authored 25 books, and more than 500 papers appeared in international refereed journals and proceedings.

Author Articles
Vector Image Retrieval Methods Based on Fuzzy Patterns

By Yevgeniya Sulema Etienne Kerre Oksana Shkurat

DOI:, 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.

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