G
George A. Papakostas
Researcher at International Hellenic University
Publications - 172
Citations - 2664
George A. Papakostas is an academic researcher from International Hellenic University. The author has contributed to research in topics: Computer science & Image moment. The author has an hindex of 25, co-authored 137 publications receiving 1943 citations. Previous affiliations of George A. Papakostas include Technological Educational Institute of Kavala & Democritus University of Thrace.
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Distance and similarity measures between intuitionistic fuzzy sets: A comparative analysis from a pattern recognition point of view
TL;DR: The main theoretical and computational properties of the measures under study are highlighted, while the relationships between them are investigated and useful conclusions are drawn regarding the accuracy and confidence of the recognition results.
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Machine Vision Systems in Precision Agriculture for Crop Farming
Efthimia Mavridou,Eleni Vrochidou,George A. Papakostas,Theodore Pachidis,Vassilis G. Kaburlasos +4 more
TL;DR: The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming, to serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture.
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A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems
TL;DR: The results are very promising because the performance of the new distance measure outperforms the corresponding performance of well‐known IFSs measures, by recognizing the patterns correctly and with high degree of confidence.
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A new class of Zernike moments for computer vision applications
TL;DR: By using Stirling's Approximation formula for the factorial and by applying some suitable mathematical properties, a novel, factorial-free direct method can be developed that is not equal to those computed by the original direct method, but they are a sufficiently accurate approximation of them.
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Fuzzy Cognitive Maps for Pattern Recognition Applications
TL;DR: A first attempt to incorporate Fuzzy Cognitive Maps, in pattern classification applications is performed, and the use of more flexible FCMs are introduced by incorporating nodes with adaptively adjusted activation functions, which gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.