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Prof. Dr. Anastasios Doulamis
Photogrammetry and Computer Vision Lab., National Technical University of Athens, Athens 15773, Greece

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0 Computer Vision
0 Image Processing
0 Machine Learning
0 Robotic Systems
0 Markovian models

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Machine Learning
Computer Vision
Signal processing and pattern analysis

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Short Biography

Prof. Anastasios Doulamis (male) received the Diploma degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA) with the highest honor and Ph.D degree in Electrical and Computer Engineering from NTUA. He is currently Associate Professor at NTUA. Professor Doulamis has received several awards and prizes during his studies, including the Best Greek Student in all fields of engineering in national level, the Best Graduate Thesis Award in the area of Electrical Engineering and several prizes from the National Technical University of Athens, the National Scholarship Foundation and the Technical Chamber of Greece. He is the author of more than 350 papers in multimedia processing and artificial intelligence and has written more than 85 journal papers. He has also more than 6100 citations in his respective field. Professor Doulamis is the Coordinator of the EU funded project H2020 TERPSICHORE and was the coordinator of FP7-4D CH World. He has served as technical coordinator of the FP7-SCOVIS EU project. He is currently involved in many European Projects like H2020 Panoptis, H2020 TERPSICHORE, H2020 Hyperion, H2020-beneffice, H2020 Waterspy, H2020 STOPIT, and FP7-eVacuate, FP7-ZoneSEc, FP7-Inachus, FP7-Experimenedia and the Greek projects Metys, CxPress, Metoera.

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Journal article
Published: 01 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Modeling ionospheric variability throughout a proper total electron content (TEC) parameter estimation is a demanding, however, crucial, process for achieving better accuracy and rapid convergence in precise point positioning (PPP). In particular, the single-frequency PPP (SF-PPP) method lacks accuracy due to the difficulty of dealing adequately with the ionospheric error sources. In order to apply ionosphere corrections in techniques, such as SF-PPP, external information of global ionosphere maps (GIMs) is crucial. In this article, we propose a deep learning model to efficiently predict TEC values and to replace the GIM-derived data that inherently have a global character, with equal or better in accuracy regional ones. The proposed model is suitable for predicting the ionosphere delay at different locations of receiver stations. The model is tested during different periods of time, under different solar and geomagnetic conditions and for stations in various latitudes, providing robust estimations of the ionospheric activity at the regional level. Our proposed model is a hybrid model comprising of a 1-D convolutional layer used for the optimal feature extraction and stacked recurrent layers used for temporal time series modeling. Thus, the model achieves good performance in TEC modeling compared to other state-of-the-art methods.

ACS Style

Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Demitris Delikaraoglou. Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. 2021. "Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 12 April 2021 in Inventions
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This paper proposes a new photonic-based non-invasive device for managing of Diabetic Foot Ulcers (DFUs) for people suffering from diabetes. DFUs are one of the main severe complications of diabetes, which may lead to major disabilities, such as foot amputation, or even to the death. The proposed device exploits hyperspectral (HSI) and thermal imaging to measure the status of an ulcer, in contrast to the current practice where invasive biopsies are often applied. In particular, these two photonic-based imaging techniques can estimate the biomarkers of oxyhaemoglobin (HbO2) and deoxyhaemoglobin (Hb), through which the Peripheral Oxygen Saturation (SpO2) and Tissue Oxygen Saturation (StO2) is computed. These factors are very important for the early prediction and prognosis of a DFU. The device is implemented at two editions: the in-home edition suitable for patients and the PRO (professional) edition for the medical staff. The latter is equipped with active photonic tools, such as tuneable diodes, to permit detailed diagnosis and treatment of an ulcer and its progress. The device is enriched with embedding signal processing tools for noise removal and enhancing pixel accuracy using super resolution schemes. In addition, a machine learning framework is adopted, through deep learning structures, to assist the doctors and the patients in understanding the effect of the biomarkers on DFU. The device is to be validated at large scales at three European hospitals (Charité–University Hospital in Berlin, Germany; Attikon in Athens, Greece, and Victor Babes in Timisoara, Romania) for its efficiency and performance.

ACS Style

Anastasios Doulamis; Nikolaos Doulamis; Aikaterini Angeli; Andreas Lazaris; Siri Luthman; Murali Jayapala; Günther Silbernagel; Adriane Napp; Ioannis Lazarou; Alexandros Karalis; Richelle Hoveling; Panagiotis Terzopoulos; Athanasios Yamas; Panagiotis Georgiadis; Richard Maulini; Antoine Muller. A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications. Inventions 2021, 6, 27 .

AMA Style

Anastasios Doulamis, Nikolaos Doulamis, Aikaterini Angeli, Andreas Lazaris, Siri Luthman, Murali Jayapala, Günther Silbernagel, Adriane Napp, Ioannis Lazarou, Alexandros Karalis, Richelle Hoveling, Panagiotis Terzopoulos, Athanasios Yamas, Panagiotis Georgiadis, Richard Maulini, Antoine Muller. A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications. Inventions. 2021; 6 (2):27.

Chicago/Turabian Style

Anastasios Doulamis; Nikolaos Doulamis; Aikaterini Angeli; Andreas Lazaris; Siri Luthman; Murali Jayapala; Günther Silbernagel; Adriane Napp; Ioannis Lazarou; Alexandros Karalis; Richelle Hoveling; Panagiotis Terzopoulos; Athanasios Yamas; Panagiotis Georgiadis; Richard Maulini; Antoine Muller. 2021. "A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications." Inventions 6, no. 2: 27.

Journal article
Published: 22 March 2021 in Sensors
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Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 ± 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 ± 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).

ACS Style

Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors 2021, 21, 2215 .

AMA Style

Athanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors. 2021; 21 (6):2215.

Chicago/Turabian Style

Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. 2021. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images." Sensors 21, no. 6: 2215.

Journal article
Published: 12 February 2021 in Journal of Cultural Heritage
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Decision making for cultural heritage protection is a multi-criteria process, involving many parameters and stakeholders. In this paper is proposed a methodological approach, for the scientific support to decision making, based on advanced software programming interfaces (Matlab and Python). As a case study, the consolidation intervention was selected. Starting point of the proposed methodology is the use of integrated documentation protocols, as a source of the required data for the monument/ object of art, capable of feeding the decision-making system with standard quality/critical parameters and risk indicators. These data are both bibliographic and real experimental values, coming from research in laboratory conditions, as well as from pilot conservation applications on monument scale. The evaluation of the crucial criteria is performed by the "experienced user/ expert" and is thus exploited by the system to adapt their weight factors through mathematical models. Main characteristics of the proposed systems is that is easy to implement, has the ability to expand to other conservation interventions, and it is executable on/compatible with different operating systems, provides a qualitative evaluation of the result.

ACS Style

A. Kioussi; M. Karoglou; E. Protopapadakis; A. Doulamis; E. Ksinopoulou; A. Bakolas; A. Moropoulou. A computationally assisted cultural heritage conservation method. Journal of Cultural Heritage 2021, 48, 119 -128.

AMA Style

A. Kioussi, M. Karoglou, E. Protopapadakis, A. Doulamis, E. Ksinopoulou, A. Bakolas, A. Moropoulou. A computationally assisted cultural heritage conservation method. Journal of Cultural Heritage. 2021; 48 ():119-128.

Chicago/Turabian Style

A. Kioussi; M. Karoglou; E. Protopapadakis; A. Doulamis; E. Ksinopoulou; A. Bakolas; A. Moropoulou. 2021. "A computationally assisted cultural heritage conservation method." Journal of Cultural Heritage 48, no. : 119-128.

Journal article
Published: 21 January 2021 in Remote Sensing
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In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground.

ACS Style

Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Evangelos Maltezos. Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery. Remote Sensing 2021, 13, 371 .

AMA Style

Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Evangelos Maltezos. Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery. Remote Sensing. 2021; 13 (3):371.

Chicago/Turabian Style

Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Evangelos Maltezos. 2021. "Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery." Remote Sensing 13, no. 3: 371.

Journal article
Published: 18 December 2020 in IEEE Open Journal of Signal Processing
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Separating the household aggregated energy consumption signal into its additive sub-components (the power signal from individual appliances), is called energy (power) disaggregation or non intrusive load monitoring (NILM). NILM resembles the signal source separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose a EnerGAN++, a model based on Generative Adversarial Networks for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method's superiority compared to the current state of the art.

ACS Style

Maria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Anastasios D. Doulamis; Eftychios Protopapadakis. EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation. IEEE Open Journal of Signal Processing 2020, 2, 1 -16.

AMA Style

Maria Kaselimi, Nikolaos Doulamis, Athanasios Voulodimos, Anastasios D. Doulamis, Eftychios Protopapadakis. EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation. IEEE Open Journal of Signal Processing. 2020; 2 (99):1-16.

Chicago/Turabian Style

Maria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Anastasios D. Doulamis; Eftychios Protopapadakis. 2020. "EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation." IEEE Open Journal of Signal Processing 2, no. 99: 1-16.

Preprint content
Published: 16 December 2020
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We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

ACS Style

Iason Katsamenis; Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis. Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images. 2020, 1 .

AMA Style

Iason Katsamenis, Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis. Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images. . 2020; ():1.

Chicago/Turabian Style

Iason Katsamenis; Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis. 2020. "Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images." , no. : 1.

Journal article
Published: 07 December 2020 in Technologies
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Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics.

ACS Style

Ioannis Markoulidakis; Ioannis Rallis; Ioannis Georgoulas; George Kopsiaftis; Anastasios Doulamis; Nikolaos Doulamis. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. Technologies 2020, 8, 76 .

AMA Style

Ioannis Markoulidakis, Ioannis Rallis, Ioannis Georgoulas, George Kopsiaftis, Anastasios Doulamis, Nikolaos Doulamis. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. Technologies. 2020; 8 (4):76.

Chicago/Turabian Style

Ioannis Markoulidakis; Ioannis Rallis; Ioannis Georgoulas; George Kopsiaftis; Anastasios Doulamis; Nikolaos Doulamis. 2020. "A Machine Learning Based Classification Method for Customer Experience Survey Analysis." Technologies 8, no. 4: 76.

Conference paper
Published: 07 December 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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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 bounding boxes around the defected region which is not adequate both for structural analysis and prefabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep learning models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform better in terms of accuracy and time and require a smaller number of annotated samples compared to other deep models, e.g. CNN. However, the final images derived are still not sufficiently accurate for structural analysis and prefabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.

ACS Style

Iason Katsamenis; Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 160 -169.

AMA Style

Iason Katsamenis, Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():160-169.

Chicago/Turabian Style

Iason Katsamenis; Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. 2020. "Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 160-169.

Journal article
Published: 20 November 2020 in Applied Sciences
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In this paper, a deep stacked auto-encoder (SAE) scheme followed by a hierarchical Sparse Modeling for Representative Selection (SMRS) algorithm is proposed to summarize dance video sequences, recorded using the VICON Motion capturing system. SAE’s main task is to reduce the redundant information embedding in the raw data and, thus, to improve summarization performance. This becomes apparent when two dancers are performing simultaneously and severe errors are encountered in the humans’ point joints, due to dancers’ occlusions in the 3D space. Four summarization algorithms are applied to extract the key frames; density based, Kennard Stone, conventional SMRS and its hierarchical scheme called H-SMRS. Experimental results have been carried out on real-life dance sequences of Greek traditional dances while the results have been compared against ground truth data selected by dance experts. The results indicate that H-SMRS being applied after the SAE information reduction module extracts key frames which are deviated in time less than 0.3 s to the ones selected by the experts and with a standard deviation of 0.18 s. Thus, the proposed scheme can effectively represent the content of the dance sequence.

ACS Style

Eftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders. Applied Sciences 2020, 10, 8226 .

AMA Style

Eftychios Protopapadakis, Ioannis Rallis, Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders. Applied Sciences. 2020; 10 (22):8226.

Chicago/Turabian Style

Eftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. 2020. "Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders." Applied Sciences 10, no. 22: 8226.

Journal article
Published: 21 May 2020 in Sustainability
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This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.

ACS Style

Anastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Konstantinos Makantasis. Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images. Sustainability 2020, 12, 4223 .

AMA Style

Anastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Konstantinos Makantasis. Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images. Sustainability. 2020; 12 (10):4223.

Chicago/Turabian Style

Anastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Konstantinos Makantasis. 2020. "Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images." Sustainability 12, no. 10: 4223.

Other
Published: 11 May 2020
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Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.Impact StatementFully Convolutional Neural Networks appear to be an accurate segmentation method in CT scans for COVID-19 pneumonia and could assist in the detection as a fast and cost-effective option.

ACS Style

Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. Deep learning models for COVID-19 infected area segmentation in CT images. 2020, 1 .

AMA Style

Athanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis. Deep learning models for COVID-19 infected area segmentation in CT images. . 2020; ():1.

Chicago/Turabian Style

Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. 2020. "Deep learning models for COVID-19 infected area segmentation in CT images." , no. : 1.

Journal article
Published: 25 April 2020 in Remote Sensing
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The necessity of predicting the spatio-temporal phenomenon of ionospheric variability is closely related to the requirement of many users to be able to obtain high accuracy positioning with low cost equipment. The Precise Point Positioning (PPP) technique is highly accepted by the scientific community as a means for providing high level of position accuracy from a single receiver. However, its main drawback is the long convergence time to achieve centimeter-level accuracy in positioning. Hereby, we propose a deep learning-based approach for ionospheric modeling. This method exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method. The scope of the proposed method is to compare and evaluate the between-satellites ionospheric delay estimation, and to aggregate the Total Electron Content (TEC) outcomes per-satellite into a single solution over the station, thus constructing regional TEC models, in an attempt to replace Global Ionospheric Maps (GIM) data. The evaluation of our proposed recurrent method for the prediction of vertical total electron content (VTEC) values is compared against the traditional Autoregressive (AR) and the Autoregressive Moving Average (ARMA) methods, per satellite. The proposed model achieves error lower than 1.5 TECU which is slightly better than the accuracy of the current GIM products which is currently about 2.0–3.0 TECU.

ACS Style

Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations. Remote Sensing 2020, 12, 1354 .

AMA Style

Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Demitris Delikaraoglou. A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations. Remote Sensing. 2020; 12 (9):1354.

Chicago/Turabian Style

Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. 2020. "A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations." Remote Sensing 12, no. 9: 1354.

Review
Published: 08 April 2020 in Movement, Time, Technology, and Art
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Performing arts and in particular dance is one of the most important domains of Intangible Cultural Heritage. However, preserving, documenting, analyzing and visually understanding choreographic patterns is a challenging task due to technical difficulties it involves. A choreography is a time-varying 3D process (4D) including dynamic co-interactions among different actors (dancers), emotional and style attributes, as well as supplementary ICH elements such as the music tempo, the rhythm, traditional costumes etc. Recent technological advancements have unleashed tremendous possibilities in capturing, documenting and storing Intangible Cultural Heritage content, which can now be generated at a greater volume and quality than ever before. The massive amounts of RGB-D and 3D skeleton data produced by video and motion capture devices. The huge number of different types of existing dances and variations dictate the need for organizing, archiving and analyzing dance-related cultural content in a tractable fashion and with lower computational and storage resource requirements. Motion capturing devices are programmable to extract humans’ skeleton data in terms of 3D points each corresponding to a human joint. This information can be combined with computer graphics software toolkits for modelling, classification and summarization purposes. In this chapter, we present recent trends in choreographic representation in terms of modelling, summarization and choreographic pose recognition. We survey recent approaches employed for the extraction of representative primitives of choreographic sequences, the recognition of choreographic pose and dance movements, as well as for the analysis and semantic representation of choreographic patterns.

ACS Style

Ioannis Rallis; Athanasios Voulodimos; Nikolaos Bakalos; Eftychios Protopapadakis; Nikolaos Doulamis; Anastasios Doulamis. Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance Analysis. Movement, Time, Technology, and Art 2020, 103 -119.

AMA Style

Ioannis Rallis, Athanasios Voulodimos, Nikolaos Bakalos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis. Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance Analysis. Movement, Time, Technology, and Art. 2020; ():103-119.

Chicago/Turabian Style

Ioannis Rallis; Athanasios Voulodimos; Nikolaos Bakalos; Eftychios Protopapadakis; Nikolaos Doulamis; Anastasios Doulamis. 2020. "Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance Analysis." Movement, Time, Technology, and Art , no. : 103-119.

Journal article
Published: 16 August 2019 in Technologies
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The convention for the safeguarding of Intangible Cultural Heritage (ICH) by UNESCO highlights the equal importance of intangible elements of cultural heritage to tangible ones. One of the most important domains of ICH is folkloric dances. A dance choreography is a time-varying 3D process (4D modelling), which includes dynamic co-interactions among different actors, emotional and style attributes, and supplementary elements, such as music tempo and costumes. Presently, research focuses on the use of depth acquisition sensors, to handle kinesiology issues. The extraction of skeleton data, in real time, contains a significant amount of information (data and metadata), allowing for various choreography-based analytics. In this paper, a trajectory interpretation method for Greek folkloric dances is presented. We focus on matching trajectories’ patterns, existing in a choreographic database, to new ones originating from different sensor types such as VICON and Kinect II. Then, a Dynamic Time Warping (DTW) algorithm is proposed to find out similarities/dissimilarities among the choreographic trajectories. The goal is to evaluate the performance of the low-cost Kinect II sensor for dance choreography compared to the accurate but of high-cost VICON-based choreographies. Experimental results on real-life dances are carried out to show the effectiveness of the proposed DTW methodology and the ability of Kinect II to localize dances in 3D space.

ACS Style

Ioannis Rallis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Georgios Bardis. Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping. Technologies 2019, 7, 56 .

AMA Style

Ioannis Rallis, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Georgios Bardis. Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping. Technologies. 2019; 7 (3):56.

Chicago/Turabian Style

Ioannis Rallis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Georgios Bardis. 2019. "Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping." Technologies 7, no. 3: 56.

Conference paper
Published: 21 February 2019 in Communications in Computer and Information Science
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In all parts of Europe Cultural Heritage represents a significant economic sector on which many communities depend. However, built CH artefacts are under continuous degradation from environmental and anthropological effects, and face significant risk of catastrophic damage from events such as flooding, earthquakes, storm and fires, which are themselves exacerbated by the impact of climate change. Action must be taken to manage these risks and mitigate their effect, however traditional approaches have been fragmented between different areas of expertise and disciplines, without regard to the combined effect of such uncoordinated interventions, leading to ineffective and often detrimental impact on the very assets they seek to protect. Therefore, in this paper we propose a new holistic approach to the effective safeguarding and management of built CH artifacts through the provision of a decision support platform based upon interdisciplinary resilience modelling of current and future risks and interventions, monitoring and analysis of CH assets and natural hazards, creation of a semantic knowledge base and vulnerability, risk and cost modelling for planning and implementing intervention strategies. The main goal of this research is to deliver an architecture that takes account of and supports mitigation of the inter-related impact of environmental, climatic and anthropogenic factors on such significant cultural heritage assets as are represented by the world heritage sites acting as primary use cases for the project. The economic impact of such interventions is also addressed, since the financial importance of tourism at these sites must be balanced with the cost of intervention to protect them whilst recognising the socio-economic benefits to be gained.

ACS Style

Anastasios Doulamis; Kyriakos Lambropoulos; Dimosthenis Kyriazis; Antonia Moropoulou. Resilient Eco-Smart Strategies and Innovative Technologies to Protect Cultural Heritage. Communications in Computer and Information Science 2019, 376 -384.

AMA Style

Anastasios Doulamis, Kyriakos Lambropoulos, Dimosthenis Kyriazis, Antonia Moropoulou. Resilient Eco-Smart Strategies and Innovative Technologies to Protect Cultural Heritage. Communications in Computer and Information Science. 2019; ():376-384.

Chicago/Turabian Style

Anastasios Doulamis; Kyriakos Lambropoulos; Dimosthenis Kyriazis; Antonia Moropoulou. 2019. "Resilient Eco-Smart Strategies and Innovative Technologies to Protect Cultural Heritage." Communications in Computer and Information Science , no. : 376-384.

Concept paper
Published: 21 December 2018 in Sensors
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In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.

ACS Style

Nikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors 2018, 19, 33 .

AMA Style

Nikolaos Doulamis, Athanasios Voulodimos, Anastasios Doulamis, Matthaios Bimpas, Aikaterini Angeli, Nikolaos Bakalos, Alessandro Giusti, Panayiotis Philimis, Antonio Varriale, Alessio Ausili, Sabato D’Auria, George Lampropoulos, Matthias Baer, Bernhard Schmauss, Stephan Freitag, Bernhard Lendl, Krzysztof Młynarczyk, Aleksandra Sosna-Głębska, Artur Trajnerowicz, Jarosław Pawluczyk, Mateusz Żbik, Jacek Kułakowski, Panagiotis Georgiadis, Stéphane Blaser, Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors. 2018; 19 (1):33.

Chicago/Turabian Style

Nikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. 2018. "WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis." Sensors 19, no. 1: 33.

Journal article
Published: 17 October 2018 in Inventions
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In modern societies, the rampant growth of the Internet, both on the technological and social level, has created fertile ground for the emergence of new types of risk. On top of that, it enhances pre-existing threats by offering new means for accessing and exploiting Critical Infrastructures. As the kinds of potential threats evolve, the security, safety and resilience of these infrastructures must be updated accordingly, both at a prevention, as well as a real-time confrontation level. Our research approaches the security of these infrastructures with a focus on the data and utilization of every possible piece of information that derives from this ecosystem. Such a task is quite daunting, since the quantity of data that requires processing resides in the Big Dataspace. To address this, we introduce a new well-defined Information Life Cycle in order to properly model and optimise the way information flows through a modern security system. This life cycle covers all the possible stages, starting from the collection phase up until the exploitation of information intelligence. That ensures the efficiency of data processing and filtering while increasing both the veracity and validity of the final outcome. In addition, an agile Framework is introduced that is optimised to take full advantage of the Information Life Cycle. As a result, it exploits the generated knowledge taking the correct sequence of actions that will successfully address possible threats. This Framework leverages every possible data source that could provide vital information to Critical Infrastructures by performing analysis and data fusion being able to cope with data variety and variability. At the same time, it orchestrates the pre-existing processes and resources of these infrastructures. Through rigorous testing, it was found that response time against hazards was dramatically decreased. As a result, this Framework is an ideal candidate for strengthening and shielding the infrastructures’ resilience while improving management of the resources used.

ACS Style

Vrettos Moulos; George Chatzikyriakos; Vassilis Kassouras; Anastasios Doulamis; Nikolaos Doulamis; Georgios Leventakis; Thodoris Florakis; Theodora Varvarigou; Evangelos Mitsokapas; Georgios Kioumourtzis; Petros Klirodetis; Alexandros Psychas; Achilleas Marinakis; Thanasis Sfetsos; Alexios Koniaris; Dimitris Liapis; Anna Gatzioura. A Robust Information Life Cycle Management Framework for Securing and Governing Critical Infrastructure Systems. Inventions 2018, 3, 71 .

AMA Style

Vrettos Moulos, George Chatzikyriakos, Vassilis Kassouras, Anastasios Doulamis, Nikolaos Doulamis, Georgios Leventakis, Thodoris Florakis, Theodora Varvarigou, Evangelos Mitsokapas, Georgios Kioumourtzis, Petros Klirodetis, Alexandros Psychas, Achilleas Marinakis, Thanasis Sfetsos, Alexios Koniaris, Dimitris Liapis, Anna Gatzioura. A Robust Information Life Cycle Management Framework for Securing and Governing Critical Infrastructure Systems. Inventions. 2018; 3 (4):71.

Chicago/Turabian Style

Vrettos Moulos; George Chatzikyriakos; Vassilis Kassouras; Anastasios Doulamis; Nikolaos Doulamis; Georgios Leventakis; Thodoris Florakis; Theodora Varvarigou; Evangelos Mitsokapas; Georgios Kioumourtzis; Petros Klirodetis; Alexandros Psychas; Achilleas Marinakis; Thanasis Sfetsos; Alexios Koniaris; Dimitris Liapis; Anna Gatzioura. 2018. "A Robust Information Life Cycle Management Framework for Securing and Governing Critical Infrastructure Systems." Inventions 3, no. 4: 71.

Journal article
Published: 09 March 2018 in Technologies
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In this paper, we scrutinize the effectiveness of classification techniques in recognizing dance types based on motion-captured human skeleton data. In particular, the goal is to identify poses which are characteristic for each dance performed, based on information on body joints, acquired by a Kinect sensor. The datasets used include sequences from six folk dances and their variations. Multiple pose identification schemes are applied using temporal constraints, spatial information, and feature space distributions for the creation of an adequate training dataset. The obtained results are evaluated and discussed.

ACS Style

Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Stephanos Camarinopoulos; Nikolaos Doulamis; Georgios Miaoulis. Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers. Technologies 2018, 6, 31 .

AMA Style

Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Stephanos Camarinopoulos, Nikolaos Doulamis, Georgios Miaoulis. Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers. Technologies. 2018; 6 (1):31.

Chicago/Turabian Style

Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Stephanos Camarinopoulos; Nikolaos Doulamis; Georgios Miaoulis. 2018. "Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers." Technologies 6, no. 1: 31.

Conference paper
Published: 16 February 2018 in MultiMedia Modeling
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This chapter describes the main research outcomes and achievements of 4D modelling in cultural heritage. 4D digital modelling implies the creation of precise time-varying 3D reconstructions of cultural heritage objects to capture temporal geometric variations/distortions, i.e., a spatio-temporal assessment. The key research challenge for 4D modelling, was the data collection over heterogeneous unstructured web resources. Such “in the wild” data include outliers and significant noise, since they have not been created for 3D modelling and reconstruction purposes. In addition, GPS and geo-information is limited or non-existent. However, such data allow for a massive reconstruction of the content even for monuments that have been destroyed due to natural phenomena or humans’ interventions. The key outcomes include (i) a Twitter-based 3D modelling of CH objects so as to reconstruct CH monuments and sites from unstructured image content, (ii) the development of a search engine and a (iii) recommendation system for different CH actors (curators, conservators, researchers), (iv) 3D reconstruction of the historic city of Calw in Germany, (v) the creation of a 3D virtual environment in real-time and (vi) launch of a 4D viewer enabling the easy handling of the 3D geometry plus the time. The results show the main innovation of the proposed 4D dimension, i.e., the time in precise modelling of the rich geometric content of the monuments.

ACS Style

Anastasios Doulamis; Nikolaos Doulamis; Eftychios Protopapadakis; Athanasios Voulodimos; Marinos Ioannides. 4D Modelling in Cultural Heritage. MultiMedia Modeling 2018, 174 -196.

AMA Style

Anastasios Doulamis, Nikolaos Doulamis, Eftychios Protopapadakis, Athanasios Voulodimos, Marinos Ioannides. 4D Modelling in Cultural Heritage. MultiMedia Modeling. 2018; ():174-196.

Chicago/Turabian Style

Anastasios Doulamis; Nikolaos Doulamis; Eftychios Protopapadakis; Athanasios Voulodimos; Marinos Ioannides. 2018. "4D Modelling in Cultural Heritage." MultiMedia Modeling , no. : 174-196.