41 citations · 75 across the 22 of their papers we have counts for
22 papers
Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
Matheus F. Kovaleski, Cristiano Premebida, João Ruivo Paulo
Active wildfire mapping from satellite imagery is challenging due to the sparse and highly imbalanced nature of fire pixels, especially in early-stage or low-density fire observati…
Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3
Matheus F. Kovaleski, Luís Garrote, Cristiano Premebida +2
Unmanned Aerial Vehicles (UAVs) have emerged as a promising platform for firefighting operations due to their flexibility, low operational cost, and ability to acquire high-resolut…
Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation
Diogo Mendonça, Tiago Barros, Cristiano Premebida +1
Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object identities and precise mask-…
Impact of 3D LiDAR Resolution in Graph-based SLAM Approaches: A Comparative Study
J. Jorge, T. Barros, C. Premebida +3
Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has be…
SPVSoAP3D: A Second-order Average Pooling Approach to enhance 3D Place Recognition in Horticultural Environments
T. Barros, C. Premebida, S. Aravecchia +2
3D LiDAR-based place recognition has been extensively researched in urban environments, yet it remains underexplored in agricultural settings. Unlike urban contexts, horticultural…
Multi-scale Feature Fusion with Point Pyramid for 3D Object Detection
Weihao Lu, Dezong Zhao, Cristiano Premebida +3
Effective point cloud processing is crucial to LiDARbased autonomous driving systems. The capability to understand features at multiple scales is required for object detection of i…
PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture
T. Barros, L. Garrote, P. Conde +4
3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. Thi…
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
Gledson Melotti, Johann J. S. Bastos, Bruno L. S. da Silva +2
Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object…
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
Pedro Conde, Rui L. Lopes, Cristiano Premebida
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a g…
Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
Nuno Cunha, Tiago Barros, Mário Reis +3
Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, a…
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Tiago Barros, Luís Garrote, Martin Aleksandrov +2
Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-…
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
Pedro Conde, Tiago Barros, Rui L. Lopes +2
With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. Thi…
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability
Jorge S. S. Júnior, Jérôme Mendes, Francisco Souza +1
Regression problems have been more and more embraced by deep learning (DL) techniques. The increasing number of papers recently published in this domain, including surveys and revi…
High-Order Conditional Mutual Information Maximization for dealing with High-Order Dependencies in Feature Selection
Francisco Souza, Cristiano Premebida, Rui Araújo
This paper presents a novel feature selection method based on the conditional mutual information (CMI). The proposed High Order Conditional Mutual Information Maximization (HOCMIM)…
Reducing Overconfidence Predictions for Autonomous Driving Perception
Gledson Melotti, Cristiano Premebida, Jordan J. Bird +2
In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs. Such layers often produce overconfident…
Probabilistic Approach for Road-Users Detection
G. Melotti, W. Lu, P. Conde +4
Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians an…
Place recognition survey: An update on deep learning approaches
Tiago Barros, Ricardo Pereira, Luís Garrote +2
Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicl…
AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
Tiago Barros, Luís Garrote, Ricardo Pereira +2
LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in le…
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
Jordan J. Bird, Diego R. Faria, Cristiano Premebida +2
The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach…
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity
Jordan J. Bird, Diego R. Faria, Anikó Ekárt +2
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification. In this work,…
Probabilistic Object Classification using CNN ML-MAP layers
G. Melotti, C. Premebida, J. J. Bird +2
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics. However, deep models often generate overconfident predictions precluding…
High-resolution LIDAR-based Depth Mapping using Bilateral Filter
C. Premebida, L. Garrote, A. Asvadi +2
High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and…