25 citations · 25 across the 8 of their papers we have counts for
8 papers
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Iason Katsamenis, Eftychios Protopapadakis, Nikolaos Bakalos +3
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying n…
Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery
Ioannis N. Tzortzis, Ioannis Rallis, Konstantinos Makantasis +3
In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such hig…
Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring
Maria Kaselimi, Eftychios Protopapadakis, Athanasios Voulodimos +2
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine lea…
Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification
Konstantinos Makantasis, Alexandros Georgogiannis, Athanasios Voulodimos +3
An increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensi…
Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation
Iason Katsamenis, Eftychios Protopapadakis, Anastasios Doulamis +2
Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place boun…
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis +2
Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we pr…
Common Mode Patterns for Supervised Tensor Subspace Learning
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis +1
In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into conside…
Tensor-based Nonlinear Classifier for High-Order Data Analysis
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis +2
In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number o…