This three day short course and workshop provides an in-depth presentation of programming tools and techniques for various computer vision and deep learning problems encountered in autonomous systems. Special attention will be paid to drones in infrastructure inspection, which is one of the main application areas of drone technologies. The same machine learning and computer vision problems do occur in other drone applications as well, e.g., for drone cinematography, land/marine surveillance, search&rescue, and 3D modeling. The short course consists of three parts (A,B,C), each having lectures and a programming workshop with hands-on lab exercises.
Part A will focus on Deep Learning. The lectures of this part provide a solid background on Deep Neural Networks (DNN) topics, notably convolutional NNs (CNNs) and deep learning for object detection. Various DNN programming tools will be presented, e.g., PyTorch, Keras, Tensorflow. The hands-on programming workshop will be on PyTorch basics and target detection with Pytorch.
Part B lectures will focus on computer vision algorithms, namely on 2D target tracking, 3D target localization techniques (giving the attendants the opportunity to master state of the art video trackers), parallel GPU, multi-core CPU architectures and GPU programming (CUDA). Two programming workshops will take place. The first one will be on CUDA programming, focusing on 2D convolution algorithms. The second one will be on how to use OpenCV (the most used library for computer vision) for target tracking.
As autonomous systems execute missions (e.g., AV inspection), Part C lectures will focus on Drone perception. Before mission execution, it is best simulated, using drone mission simulation tools. Such simulations will be presented using AirSim. Additionally a programming workshop on ROS and Gazebo simulations for drones will take place.
The lectures and programming tools will provide programming skills for the various computer vision and deep learning problems encountered in drone inspection, which is one of the main application areas of drone technologies. The same machine learning and computer vision problems do occur in other drone applications as well, e.g., for drone cinematography, land/marine surveillance, search&rescue.
Lectures and programming workshops will be in English. PDF files will be available at the end of the course.
Part A (8 hours), Deep learning sample topic list
- Deep neural networks. Convolutional NNs.
- Deep learning for target detection.
- PyTorch basics.
- Target detection with PyTorch.
- Object oriented Tensorflow in Google Colab.
Part B (8 hours), Computer vision sample topic list
- 2D target tracking and 3D target localization.
- Parallel GPU and multi-core CPU architectures. GPU programming.
- CUDA programming.
- OpenCV programming for object tracking.
Part C (8 hours), Drone perception sample topic list
- Drone mission simulations.
- Drone perception and estimation.
- ROS UAL Abstraction Layer for drone applications.
- Drone perception with ROS.
The course will take place on 19-21 August 2020.
All lectures and workshops will take place at KEDEA, 3is Spetemvriou – Panepistimioupoli, 54636, Thessaloniki, Greece.
You can find additional information about the city of Thessaloniki and details on how to get to the city here.
Each registrant will use her/his own computer for a) participating in the course (in the remote mode) and b) for running the programming exercises (both in the remote mode, but also in the physical course mode due to COVID-19 considerations). Details on the minimal HW/SW configuration will be posted here very soon.
|Topic||Deep Learning||Computer Vision||Autonomous systems planning and control|
|8:30-9:00||Introduction to autonomous systems
|9:00-10:00||Deep neural networks – Convolutional NNs||2D target tracking
||Drone mission simulations.|
|10:00-11:00||Deep learning for target detection||Parallel GPU and multi-core CPU architectures – GPU programming.|| Drone perception and estimation.
|11:00-11:30||Coffee break||Coffee break||Coffee break|
Object detection, image synthesis and style transfer on images using PyTorch.
|CUDA programming.||ROS UAL Abstraction Layer for drone applications.
|13:30-14:30||Lunch break||Lunch break||Lunch break|
Understand the core functionalities of an object detector. Training and deployment.
|OpenCV programming for object tracking
||Drone perception with ROS
|16:30-18:30||Object oriented Tensorflow in Google Colab.||Drone mission simulations in Airsim.|
|20:00||Welcome party||Goodbye party|
* Eastern European Summer Time (EEST)
** This programme is indicative and may be modified without prior notice by announcing (hopefully small) changes in lectures/lecturers.
Early registration (till 30/06/2020):
• Standard: 200 Euros
• Reduced registration for young professionals (up to 2 years after graduation): 100 Euros
• Unemployed or Undergraduate/MSc/PhD student*: 50 Euros
Later or on-site registration (after 30/06/2020):
• Standard: 210 Euros
• Reduced registration for young professionals (up to 2 years after graduation): 110 Euros
• Unemployed or Undergraduate/MSc/PhD student*: 60 Euros
*Proof of employment/unemployment or student status should be provided upon registration.
If by clicking the <<Register Now!>> button the page opens in Greek language, please find on the upper right part of the page the option to change it in English. (it is next to the search field)
All lectures and workshops will be in English.
A certificate of attendance will be provided.
*** Due to the special COVID-19 circumstances, the 2020 edition of the «Programming short course and workshop on Deep Learning and Computer Vision for Autonomous Systems» will take place primarily as web course on 19-21 August 2020 (default mode). Remote participation will be available via teleconferencing. ***
- 70% refund for cancellation up to 15/06/2020
- 50% refund for cancellation up to 15/07/2020
- 0% refund afterwards
Every effort will be undertaken to run the course as planned. However, due to the special COVID-19 circumstances, the organizer (AUTH) reserves right to cancel the event anytime by simple notice to the registrants (by email by announcing it in the course www page). In this case, each registrant will be reimbursed 100% for the registration fee. However, the organizer will be not held liable for any other loss incurred to the registrants (e.g., for air tickets, hotels or any other travel arrangements).
Presentations and lab notes will be available to the attendees.
Part A (first day, 2 lectures, 2 programming exercises) 19/08/2020
The lectures of Part A provide a solid background on the topics of Deep neural networks. Convolutional NNs and deep learning for object detection. Various DNN programming tools will be presented, e.g., PyTorch, Keras, Tensorflow.
The hands-on programming workshop will be on PyTorch basics and target detection with PyTorch.
1. Deep neural networks. Convolutional NNs:
Abstract: From multi-layer Perceptrons to deep architectures. Fully connected layers. Convolutional layers. Tensors and mathematical formulations. Pooling. Training convolutional NNs. Initialization. Data augmentation. Batch Normalization. Dropout. Deployment on embedded systems. Lightweight deep learning. DNN programming tools (e.g., PyTorch, Keras, Tensorflow).
2. Deep learning for target detection:
Abstract: Recently, Convolutional Neural Networks (CNNs) have been used for object/target (e.g., car, pedestrian, road sign) detection with great results. However, using such CNN models on embedded processors for real-time processing is prohibited by HW constraints. In that sense various architectures and settings will be examined in order to facilitate and accelerate the use of embedded CNN-based object detectors with limited computational capabilities. The following target detection topics will be presented: Object detection as search and classification task. Detection as classification and regression task. Modern architectures for target detection (e.g., RCNN, Faster-RCNN, YOLO, SSD). Lightweight architectures. Data augmentation. Deployment. Evaluation and benchmarking.
3. PyTorch basics:
Abstract: Introduction to PyTorch, simple commands, learn how to build an Image Classifier, generate fake images using GANs and transfer style between images using PyTorch.
4. Target detection with PyTorch:
Abstract: Given some parts of the code, build and train an object detector in PyTorch. Dataset preparation, data loaders, dealing with unequal number of boxes for each image, understanding the core functionality of an object detector. Training and deployment.
5. Object oriented Tensorflow in Google Colab:
Abstract: Online source code examples and hands-on challenge using Google Colaboratory. Introduction to Tensorflow (declare tensors, run sessions, visualization), object oriented wrappers for tensors (dense and convolutional layer wrappers, neural network inference, load/save model parameters, gradient descent), train a state-of-the-art CNN classifier in CIFAR10 (data pre-processing and augmentation, batch normalization, advanced activation functions, optimizers, L2 regularization). Latest developments in Tensorflow (Tensorflow 2.0, tensorflow.js, TensorRT.)
Part B (second day, 2 lectures, 2 programming exercises) 20/08/2020:
Part B lectures will focus on computer vision algorithms, namely on 2D target tracking, 3D target localization techniques (giving the attendants the opportunity to see state of the art video trackers), parallel GPU and multi-core CPU architectures and GPU programming (CUDA). Two programming workshops will take place. The first one will be on CUDA programming, focusing on 2D convolution algorithms. The second one will be on how to use OpenCV (the most used library for computer vision) for target tracking.
1. 2D target tracking:
Abstract: Target tracking is a crucial component of many computer vision systems. Many approaches regarding face/object detection and tracking in videos have been proposed. In this lecture, video tracking methods using correlation filters or convolutional neural networks are presented, focusing on video trackers that are capable of achieving real time performance for long-term tracking on a UAV platform.
2. Parallel GPU and multi-core CPU architectures . GPU programming:
Abstract: GPU’s unique architectural features are emphasized through CPU-GPU comparison. GPU’s architecture in terms of ALUs and memory types is given in detail in order to introduce the GPU’s programming special characteristics. The audience becomes familiar with terms such as grid, block, thread, kernel, etc. and the general layout of a CUDA program is presented. Cuda keywords are explained by presenting simple CUDA programs. Finally, areas where GPU programming achieves outstanding performance are mentioned and 2D convolution algorithm implementations are demonstrated.
3. CUDA programming:
Abstract: 2D and 3D convolutions are very important tools both for computer vision (e.g., for target tracking and for deep learning (convolutional NNs). Learn how to implement a 2D convolution between an image and a mask with CUDA.
4. OpenCV programming for object tracking:
Abstract: The first part of this tutorial will have an introduction to the OpenCV library using Python. Students can learn how to perform basic image processing operations, such as reading and displaying an image, extracting ROIs, applying filters etc. In the second part of the tutorial, the students will learn how to perform visual object tracking in video sequences, with correlation filter based tracking algorithms and OpenCV.
Part C (third day, 2 lectures, 1 programming exercise) 21/08/2020:
Drone planning and control
As drones execute missions (e.g., AV shooting, inspection), Part C lectures will focus on drone perception. Before mission execution, it is best simulated, using drone mission simulation. Such simulations will be presented using AirSim. Additionally a programming workshop on ROS and Gazebo simulations for drones will take place.
1. Drone mission simulations:
Abstract: Machine learning algorithms need large amounts of quality data to be trained efficiently. Gathering and annotating that sheer amount of data is a time-consuming and error-prone task. Those problems limit scale and quality. Synthetic data generation has become increasingly popular due to fast generation and automatic annotation.
2. Drone perception and estimation:
Abstract: Abstract to be announced soon.
3. ROS UAL Abstraction Layer for drone applications:
Abstract: Abstract to be announced soon.
4. Drone perception with ROS:
Abstract: Abstract to be announced soon.
IF I HAVE A QUESTION?
LECTURERS & TUTORS
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and Ph.D.Degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities. His current interests are in the areas of image/video processing, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He is currently leading the big European H2020 R&D project MULTIDRONE. He is also chair of the Autonomous Systems initiative. (Lecture: Introduction to drone imaging.)
Jesús Capitán is Assistant Professor at the University of Seville. He received his degree in Telecommunication Engineering (2006) from the University of Seville, and a Ph.D. in Robotics (2011) from the same university. In 2005 he joined the Robotics, Vision and Control Research Group. During his Ph.D., he worked as a visiting fellow at the Robotics Institute, Carnegie Mellon University, Pittsburgh, U.S.A.; and the Institute for Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal. After his Ph.D., he worked as a senior researcher at the Institute for Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal (2011-2012) and the Networked Embedded System Group, University of Duisburg-Essen, Essen, Germany (2012-2013). His research is focused on cooperative multi-robot systems. In particular, he is interested in decentralized decision-making, planning under uncertainty, cooperative active perception and Partially Observable Markov Decision Processes. (Lecture: Drone mission planning and control.)
Arturo Torres-González is a Postdoc Researcher at the University of Seville. He received his degree in Telecommunication Engineering (2011) from the University of Seville, and a Ph.D. in Robotics (2017) from the same university. In 2010 he joined the Robotics, Vision and Control Research Group. During his Ph.D., he worked as a visiting fellow at the Australian Center for Field Robotics, University of Sydney, Sydney, Australia. He obtained the Best Iberian Thesis in Robotics Award 2017 by Spanish and Portuguese robotics societies SEIDROB and SPR. His research is focused on multi-agent systems, robot-sensor network cooperation and robot localization and mapping. (Programming workshop: Drones with ROS and Gazebo simulations.)
Paraskevi Nousi obtained her BsC in Informatics in 2014 from Aristotle University of Thessaloniki and is currently pursuing her PhD in Computational Intelligence at the Informatics Department of Aristotle University of Thessaloniki. Her research is focused on developing effective and efficient Deep Learning methods for visual analysis tasks, such as Visual Object Tracking, Object Detection and Recognition and has been influenced by the needs of the H2020 project MULTIDRONE. (Lecture: Deep learning for target detection. Programming workshop: PyTorch: Understand the core functionalities of an object detector. Training and deployment.)
Iason Karakostas received the Diploma of Electrical Engineering in 2017 and is currently a PhD Student at the Artificial Intelligence and Information Analysis Laboratory (AIIA) in the Department of Informatics of AUTH. He has co-authored 2 papers in international conferences and has participated in a European Union-funded R&D project. His current research interests include machine learning, computer vision, autonomous robotics and intelligent cinematography. (Lecture: 2D target tracking. Programming workshop: OpenCV programming for object tracking.)
Educational record of Prof. I. Pitas
Prof I. Pitas was Visiting/Adjunct/Honorary Professor/Researcher and lectured at several Universities: University of Toronto (Canada), University of British Columbia (Canada), EPFL (Switzerland), Chinese Academy of Sciences (China), University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, Henan University (China). He delivered 90 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He ran 17 short courses and tutorials on Autonomous Systems, Computer Vision and Machine Learning, most of them in the past 3 years in many countries, e.g., USA, UK, Italy, Finland, Greece, Australia, N. Zealand, Korea, Taiwan, Sri Lanka, Bhutan.
PAST COURSE EDITIONS
Participants: 53, Countries: UK, Germany, Sweden, Norway, Italy, Greece, Croatia, Slovakia.
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SAMPLE COURSE MATERIAL. RELATED LITERATURE
1) Multidrone Project (MULTIple DRONE platform for media production), funded by the EU (2017-19), within the scope of the H2020 framework, https://multidrone.eu/
2) Semi-Supervised Subclass Support Vector Data Description for image and video classification, V. Mygdalis, A. Iosifidis, A. Tefas, I. Pitas, Neurocomputing, vol. 291, pp. 237-241, 2018
3) Face detection Hindering, P. Chriskos, J. Munro, V. Mygdalis, I. Pitas, Proceedings of the IEEE Global Conference on Signal and Information Processing (GLOBALSIP), Quebec, Montreal, 2017
4) 2D visual tracking for sports UAV cinematography applications, O. Zachariadis, V. Mygdalis, I. Mademlis, I. Pitas, Proceedings of the IEEE Global Conference on Signal and Information Processing (GLOBALSIP), Montreal, Canada, 2017
5) Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance, N. Tsapanos, A. Tefas, N. Nikolaidis and I. Pitas, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 30, issue 1, pp. 284-294, 2019
6) Convolutional Neural Networks for Visual Information Analysis with Limited Computing Resources, P. Nousi, E. Patsiouras, A. Tefas, I. Pitas, Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018
7) Overview of drone cinematography for sports filming, I. Mademlis, V. Mygdalis, C. Raptopoulou, N.Nikolaidis, N. Heise, T. Koch, T. Wagner, A. Messina, F. Negro, S. Metta, I.Pitas, European Conference on Visual Media Production (CVMP), London, UK, 2017
8) Challenges in Autonomous UAV cinematography: An overview, I. Mademlis, V. Mygdalis, N. Nikolaidis, I. Pitas, Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Diego, USA, 2018
9) Learning Multi-graph regularization for SVM classification, V.Mygdalis, A.Tefas, I.Pitas, Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018
10) UAV Cinematography Constraints Imposed by Visual Target Trackers, I. Karakostas, I. Mademlis, N. Nikolaidis, I. Pitas, Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018
11) Efficient camera control using 2D visual information for unmanned aerial vehicle-based cinematography, N. Passalis, A. Tefas, I. Pitas, Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 2018
12) The future of media production through multi-drones’ eyes, A. Messina, S. Metta, M. Montagnuolo, F. Negro, V. Mygdalis, I. Pitas, J. Capitán, A. Torres, S. Boyle, D. Bull, F. Zhang, International Broadcasting Convention (IBC), Amsterdam, Netherlands, 2018
13) Quality Preserving Face De-Identification Against Deep CNNs, P. Chriskos, R. Zhelev, V. Mygdalis, I. Pitas, Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, 2018
14) Improving Face Pose Estimation using Long-Term Temporal Averaging for Stochastic Optimization, N. Passalis, A. Tefas, Proceedings of the International Conference on Engineering Applications of Neural Networks, EANN 2017, Athens, Greece, 2017
15) Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition, P. Nousi, A. Tefas, Proceedings of the International Conference on Engineering Applications of Neural Networks, EANN, Athens, Greece, 2017
16) Concept Detection and Face Pose Estimation Using Lightweight Convolutional Neural Networks for Steering Drone Video Shooting, N. Passalis, A. Tefas, Proceedings of the European Signal Processing Conference (EUSIPCO), Kos, Greece, 2017
17) Human Crowd Detection for Drone Flight Safety Using Convolutional Neural Networks, M.Tzelepi, A.Tefas, Proceedings of the European Signal Processing Conference (EUSIPCO), Kos, Greece, 2017
18) Lightweight Two-Stream Convolutional Face Detection, D. Triantafyllidou, P. Nousi, A. Tefas, Proceedings of the European Signal Processing Conference (EUSIPCO), Kos, Greece, August, 2017
19) Fast Deep Convolutional Face Detection in the Wild Exploiting Hard Sample Mining, D. Triantafyllidou, P. Nousi, A. Tefas, Big Data Research, Elsevier, vol. 11, pp. 65-76, 2018
20) Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks, N. Passalis, A. Tefas, Proceedings of the International Conference on Computer Vision (ICCV), Venice, Italy, 2017
21) Self-Supervised Auto-encoders for Clustering and Classification, P. Nousi, A. Tefas, Evolving Systems Journal, Springer, pp 1–14, 2018
22) Unsupervised Knowledge Transfer using Similarity Embeddings, N. Passalis, A. Tefas, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 30, issue 3, pp. 946-950, 2018
23) Recurrent Attention for Deep Neural Object Detection, G. Symeonidis, A. Tefas, Hellenic Conference on Artificial Intelligence (SETN), Rio Patras, Greece, 2018
24) Neural Network Knowledge Transfer using Unsupervised Similarity Matching, N. Passalis, A. Tefas, Proceedings of the International Conference on Pattern Recognition (ICPR), Beijing, China, 2018
25) Deep reinforcement learning for frontal view person shooting using drones, N. Passalis, A. Tefas, Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Rhodes, Greece, 2018
26) A Multidrone Approach for Autonomous Cinematography Planning, A. Torres-Gonzalez, J. Capitan, R. Cunha, A. Ollero and I. Mademlis, Proceedings of the Iberian Robotics Conference (ROBOT), 2017
27) Decentralized safe conflict resolution for multiple robots in dense scenarios, E. Ferrera, J. Capitán, A.R. Castaño and P.J. Marrón, Robotics and Autonomous Systems, vol. 91, pp. 179-193, 2017
28) Cooperative perimeter surveil using Bluetooth framework under communication constraints, J.M. Aguilar, P. R. Soria, B.C. Arrue and A. Ollero, Proceedings of the Iberian Robotics Conference (ROBOT), 2017
29) Applying Frontier Cells Based Exploration and Lazy Theta* Path Planning over Single Grid-Based World Representation for Autonomous Inspection of Large 3D Structures with an UAS, M. Faria, I. Maza and A. Viguria, Journal of Intelligent & Robotic Systems, accapted Springer
30) Discriminative Optimization: Theory and Applications to Computer Vision Problems, J. Vongkulbhisal, F. De la Torre, and J. P. Costeira, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 41, issue 4, pp. 829 – 843, 2018
31) Integrated Visual Servoing Solution to Quadrotor Stabilization and Attitude Estimation Using a Pan and Tilt Camera, D. Cabecinhas, S. Brás, R. Cunha, C. Silvestre, P. Oliveira, IEEE Transactions on Control Systems Technology, vol. 27, issue 1, pp. 14-29, 2017
32) UAL: An Abstraction Layer for Unmanned Aerial Vehicles, F. Real, A. Torres-González, P. Ramón-Soria, J. Capitán and A. Ollero, Proceedings of the International Symposium on Aerial Robotics (ISAR), Philadelphia, PA, USA, 2018
33) Inverse Composition Discriminative Optimization for Point Cloud Registration, J. Vongkulbhisal, B. I. Ugalde, F. De la Torre, J. P. Costeira, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018
34) P. Chriskos, O.Zoidi, A.Tefas and I.Pitas, De-identifying facial images using singular value decomposition and projections, Multimedia Tools and Applications, Springer, vol. 76, issue 3, pp. 3435-3468, 2017
35) Cooperative Unmanned Aerial Systems for Fire Detection, Monitoring and Extinguishing, L. Merino, J.R. Martinez-de Dios, A. Ollero, In «Handbook of Unmanned Aerial Vehicles», ISBN 978-90-481-9706-4, Springer, 2015
36) Shot Type Feasibility in Autonomous UAV Cinematography, I. Karakostas, I. Mademlis, N. Nikolaidis, I. Pitas, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019
37) High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events, I. Mademlis, V. Mygdalis, N. Nikolaidis, M. Montagnuolo, F. Negro, A. Messina, I. Pitas, IEEE Transactions on Broadcasting, accepted for publication, 2019
38) Autonomous Unmanned Aerial Vehicles Filming in Dynamic Unstructured Outdoor Environments, I. Mademlis, N. Nikolaidis, A. Tefas, I. Pitas, T. Wagner, A. Messina,
IEEE Signal Processing Magazine, vol. 36, issue 1, pp. 147-153, 2019
39) Deep Convolutional Feature Histograms for Visual Object Tracking, P. Nousi, A. Tefas, I. Pitas, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019
40) Semantic Map Annotation Through UAV Video Analysis Using Deep Learning Models in ROS, E. Kakaletsis, M. Tzelepi, P.I. Kaplanoglou, C. Symeonidis, N. Nikolaidis, A. Tefas, I. Pitas, Proceedings of the International Conference on Multimedia Modeling (MMM), Thessaloniki, Greece, 2019
41) Exploiting multiplex data relationships in Support Vector Machines, V. Mygdalis, A. Tefas, I. Pitas, Pattern Recognition, Elsevier, vol. 85, pp. 70-77, 2019
42) Deep reinforcement learning for controlling frontal person close-up shooting, N. Passalis, A. Tefas, Neurocomputing, Elsevier, vol. 335, pp. 37-47, 2019
43) Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety, M. Tzelepi, A. Tefas, IEEE Transactions on Emerging Topics in Computational Intelligence, accepted for publication, 2019
44) Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling, N. Passalis, A. Tefas, IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, 2018
Prof. Ioannis Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el
Multidrone project: https://multidrone.eu/
Icarus Research Team: http://icarus.csd.auth.gr/
Laboratory of Artificial Intelligence and Information Analysis: http://www.aiia.csd.auth.gr/
Department of Informatics, Aristotle University of Thessaloniki (AUTH): http://www.csd.auth.gr/en/