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Charalampos Symeonidis
Researcher at Aristotle University of Thessaloniki
Publications - 14
Citations - 49
Charalampos Symeonidis is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 5 publications receiving 14 citations.
Papers
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Proceedings ArticleDOI
Vision-based UAV Safe Landing exploiting Lightweight Deep Neural Networks
Charalampos Symeonidis,Efstratios Kakaletsis,Ioannis Mademlis,Nikos Nikolaidis,Anastasios Tefas,Ioannis Pitas +5 more
TL;DR: In this article, the authors proposed a UAV safe landing navigation pipeline that relies on lightweight computer vision modules, able to be executed on the limited computational resources on-board a typical UAV.
Proceedings ArticleDOI
Improving Neural Non-Maximum Suppression for Object Detection by Exploiting Interest-Point Detectors
TL;DR: Neural NMS performance is augmented by feeding the network additional information extracted from the appearance of each candidate ROI, which captures statistical properties regarding the spatial distribution of interest-points detected within the corresponding image region.
Book ChapterDOI
Semantic Map Annotation Through UAV Video Analysis Using Deep Learning Models in ROS
Efstratios Kakaletsis,Maria Tzelepi,Pantelis I. Kaplanoglou,Charalampos Symeonidis,Nikos Nikolaidis,Anastasios Tefas,Ioannis Pitas +6 more
TL;DR: This work proposes an automatic annotation of 3D maps with crowded areas, by projecting 2D annotations that are derived through visual analysis of UAV video frames, and provide semantic heatmaps that are projected on the 3D occupancy grid of Octomap.
BookDOI
Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization
Charalampos Symeonidis,Paraskevi Nousi,P. Tosidis,Konstantinos Tsampazis,Nikolaos Passalis,Anastasios Tefas,Nikos Nikolaidis +6 more
TL;DR: In this paper, a method that automatically generates realistic synthetic data with annotations for a) person detection, b) face recognition, and c) human pose estimation is presented. But it is not suitable for the task of human-centric perception, since the collection and distribution of such data may also face restrictions due to legislation regarding privacy.
Book ChapterDOI
Efficient Realistic Data Generation Framework Leveraging Deep Learning-Based Human Digitization
Charalampos Symeonidis,Paraskevi Nousi,P. Tosidis,Konstantinos Tsampazis,Nikolaos Passalis,Anastasios Tefas,Nikos Nikolaidis +6 more
TL;DR: In this article, a method that automatically generates realistic synthetic data with annotations for a) person detection, b) face recognition, and c) human pose estimation is presented. But it is not suitable for the task of human-centric perception, since the collection and distribution of such data may also face restrictions due to legislation regarding privacy.