Welcome to the

HUman-MAchines INteraction Laboratory (HUMAIN-Lab)

Welcome to the

HUman-MAchines INteraction Laboratory (HUMAIN-Lab)

Welcome to the

HUman-MAchines INteraction Laboratory (HUMAIN-Lab)

Welcome to the

HUman-MAchines INteraction Laboratory (HUMAIN-Lab)

Scope:
Machines, implemented either in hardware or in software, with whom a human interacts proliferate, furthermore their complexity increases; for instance, the aforementioned machines include devices for communication, computers, robots, costumer service software, and other. This lab pursues the study, analysis and design of both hardware and software that enables the seamless collaboration of humans with machines.
Objectives

Innovation

The operation of a machine in the “physical” environment is typically supported by arithmetic models. However, when a human is involved there might emerge non-numerical data…

Inventiveness

Our orientation is toward the development of machines with a capacity to interact with humans in various applications including education, precision farming, patrolling in the physical environment and other.

Entrepreneurship

Our orientation is toward the conversion of our laboratory prototypes in commercial products.

Theodoros Pachidis

Expertise: Robotics and Software Engineering

Michail Manios

Technical Staff
Homepage

Two fuzzy lattice reasoning (FLR) classifiers and their application for human facial expression recognition – Journal of Multiple-Valued Logic and Soft Computing, vol. 22, no. 4-6

S.E. Papadakis, V.G. Kaburlasos, G.A. Papakostas, “Two fuzzy lattice reasoning (FLR) classifiers and their application for human facial expression recognition”, Journal of Multiple-Valued Logic and Soft Computing, vol. 22, no. 4-6, pp. 561-579, 2014 (Special Issue on Uncertainty Modeling in Knowledge Engineering and Decision Making. Guest Editors: Cengiz Kahraman and Farouk Yalaoui).

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A proposal of texture features for interactive CTA segmentation by active learning – InMed 2014, San Sebastian, Spain

J. Maiora, G.A. Papakostas, V.G. Kaburlasos, M. Graña, “A proposal of texture features for interactive CTA segmentation by active learning”, KES International Conference on Innovation in Medicine and Healthcare (InMed-14), San Sebastian, Spain, 9-11 July 2014, pp. 311-320.

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Distance and similarity measures between intuitionistic fuzzy sets: a comparative analysis from a pattern recognition point of view – Pattern Recognition Letters, vol. 34, no. 14

G.A. Papakostas, A.G. Hatzimichailidis, V.G. Kaburlasos, “Distance and similarity measures between intuitionistic fuzzy sets: a comparative analysis from a pattern recognition point of view”, Pattern Recognition Letters, vol. 34, no. 14, pp. 1609-1622, 2013.

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