Vasiliki Balaska

Democritus University of Thrace | DUTH · Department of Production and Management Engineering

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs...

In numerous real-world situations, acquiring extensive sets of labeled data poses a formidable challenge. This study introduces an innovative approach for enhancing weakly supervised learning through anomaly-informed weighted training. The method not only is tested in diverse benchmark datasets such as CIFAR-10 and Fashion-MNIST by simulating a bin...

In computer vision, datasets and benchmarks are widely used to compare algorithms and boost scientific progress. Especially in the human action recognition research field, extracting dance poses from video sequences for fragmentation and recognition of dancer movements is a challenging task, and new datasets are always important. This paper present...

The digitalization of traditional industrial processes has profoundly influenced every step of the manufacturing value chain during the past two decades, having as its main goal to achieve zero-defected products. Moreover, since dairy production is at the heart of food industry, it is critical to leverage innovative technologies to increase their e...

Agriculture 5.0 refers to the next phase of agricultural development, building upon the previous digital revolution in the agrarian sector and aiming to transform the agricultural industry to be smarter, more effective, and ecologically conscious. Farming processes have already started becoming more efficient due to the development of digital techn...

In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a we...

The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors...

Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an Oc...

The explosion of the digitisation of traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. Therefore, it is crucial to leverage modern a...

The occurring growth in e-commerce comes along with an increasing number of first-time delivery failures due to the customer’s absence at the delivery location. Failed deliveries result in rework, causing a significant impact on the carriers’ delivery cost. Hence, the last mile is the portion of a journey that involves moving people and commodities...

Over the previous two decades, a tremendous impact has been created on each stage of the production value chain, through digitization of the traditional industrial processes and procedures. Since warehouses are at the heart of distributed supply chain networks, it is critical to leverage modern automation tools and through-engineering solutions to...

The explosion of logistics is consolidating a positive impact on modern society by offering a critical contribution to its economic development. Traditional inventory methods tend to be transformed based on the rapid development of artificial intelligence technology. In particular, changing the way inventories are recorded, through the evolution of...

Semantic interpretation of regions or entities is increasingly attracting the attention of scholars, owing to its vast applicability in several disciplines. In this context, modern autonomous systems are capable to semantically recognize and separate entities from camera measurements, while effectively interprete and interact with their environment...

Owing to its vast applicability, the semantic interpretation of regions or entities is increasingly attracting the attention of scholars in the robotics community. Recent research in robot vision has equipped, modern autonomous systems with the ability to semantically recognize and segment entities from scenes with the aim to effectively interpret...

Loop-closure detection (LCD) has become an essential part of any simultaneous localization and mapping (SLAM) framework. It provides a means to rectify the drift error, which is typically accumulated along a robot’s trajectory. In this article we propose an LCD method based on tracked visual features, combined with a signal peak-trace filtering app...

The paper at hand introduces a novel system for producing an enhanced semantic map that leverages a reconstruction approach of street-view scenes using computer vision and machine learning techniques. Focusing on the recognition and localization of objects/entities, the composed map combines semantic information from publicly available, yet of lowe...

Due to its vast applicability, the semantic interpretation of regions or entities increasingly attracts the attention of scholars within the robotics community. The paper at hand introduces a novel unsupervised technique to semantically identify the position of an autonomous agent in unknown environments. When the robot explores a certain path for...

This work deals with the graph-based semantic segmentation of a robot’s traversed environment using the Louvain algorithm. In recent years, semantic segmentation has been the focus of several researchers’ interest and is applied to a variety of robotic applications. The Louvain method for community detection is a novel technique for extracting comm...