62 citations · 377 across the 29 of their papers we have counts for
38 papers
BMASH: Ball-Motion-Aware Soccer Header Spotting
Ahmed Endris Hasen, Muhammad Shahzad Khan, Nikolaos Passalis +1
Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety applications. In soccer, howe…
MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
Ahmed Endris Hasen, Nikolaos Passalis, Tomi Vanttinen +1
Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and performance analysis. In ham…
AquaMonitor: A multimodal multi-view image sequence dataset for real-life aquatic invertebrate biodiversity monitoring
Mikko Impiö, Philipp M. Rehsen, Tiina Laamanen +3
This paper presents the AquaMonitor dataset, the first large computer vision dataset of aquatic invertebrates collected during routine environmental monitoring. While several large…
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparison
Mikko Impiö, Philipp M. Rehsen, Jenni Raitoharju
The amount of image datasets collected for environmental monitoring purposes has increased in the past years as computer vision assisted methods have gained interest. Computer visi…
Improving Taxonomic Image-based Out-of-distribution Detection With DNA Barcodes
Mikko Impiö, Jenni Raitoharju
Image-based species identification could help scaling biodiversity monitoring to a global scale. Many challenges still need to be solved in order to implement these systems in real…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
Habitat classification from satellite observations with sparse annotations
Mikko Impiö, Pekka Härmä, Anna Tammilehto +2
Remote sensing benefits habitat conservation by making monitoring of large areas easier compared to field surveying especially if the remote sensed data can be automatically analyz…
A multimodal stress detection dataset with facial expressions and physiological signals
Majid Hosseini, Fahad Sohrab, Raju Gottumukkala +5
Affective computing has garnered the attention and interest of researchers in recent years, as there is a need for AI systems to better understand and react to human emotions. Howe…
Automatic Main Character Recognition for Photographic Studies
Mert Seker, Anssi Männistö, Alexandros Iosifidis +1
Main characters in images are the most important humans that catch the viewer's attention upon first look, and they are emphasized by properties such as size, position, color satur…
Multi-level reversible encryption for ECG signals using compressive sensing
Mikko Impiö, Mehmet Yamaç, Jenni Raitoharju
Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in ot…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
Mert Seker, Anssi Männistö, Alexandros Iosifidis +1
The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of th…
Ensembling object detectors for image and video data analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We fur…
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Mohammad Soltanian, Junaid Malik, Jenni Raitoharju +3
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications. However, employed recognition models usually follow the methodol…
Graph Embedding with Data Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data…
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Jorge Peña Queralta, Jussi Taipalmaa, Bilge Can Pullinen +6
Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support…
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore…
AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing
Jorge Peña Queralta, Jenni Raitoharju, Tuan Nguyen Gia +2
Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents t…
Probabilistic Color Constancy
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +3
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene…
Saliency-based Weighted Multi-label Linear Discriminant Analysis
Lei Xu, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for d…
Not all domains are equally complex: Adaptive Multi-Domain Learning
Ali Senhaji, Jenni Raitoharju, Moncef Gabbouj +1
Deep learning approaches are highly specialized and require training separate models for different tasks. Multi-domain learning looks at ways to learn a multitude of different task…
Ellipsoidal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data…
aColor: Mechatronics, Machine Learning, and Communications in an Unmanned Surface Vehicle
Jose Villa, Jussi Taipalmaa, Mikhail Gerasimenko +13
The aim of this work is to offer an overview of the research questions, solutions, and challenges faced by the project aColor ("Autonomous and Collaborative Offshore Robotics"). Th…
Boosting rare benthic macroinvertebrates taxa identification with one-class classification
Fahad Sohrab, Jenni Raitoharju
Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be sca…