[19] JSS Junior, J Mendes, F Souza, C Premebida (2024). “Distilling Complex Knowledge Into Explainable TS Fuzzy Systems”. IEEE Transactions on Fuzzy Systems.

[18] P. Conde, R. Lopes, C. Premebida (2024). “Improving Accuracy and Calibration of Deep Image Classifiers with Agreement-Driven Dynamic Ensemble”. IEEE Open Journal of the Computer Society.

[17] T Barros, L Garrote, P Conde, MJ Coombes, C Liu, C Premebida, UJ Nunes (2024). “PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture”. IEEE Robotics and Automation Letters.

[16] Jorge S.S. Junior, J. Mendes, F. Souza, C. Premebida (2023). “Survey on Deep Fuzzy Systems in regression applications: a view on interpretability”. International Journal of Fuzzy Systems, Springer.

[15] G. Melotti, W Lu, P Conde, D Zhao, A. Asvadi, N Gonalves, C. Premebida (2023). “Probabilistic Approach for Road-Users Detection”. IEEE Transactions on Intelligent Transportation Systems.

[14] W Lu, D Zhao, C Premebida, L Zhang, W Zhao, D Tian (2023). “Improving 3D Vulnerable Road User Detection with Point Augmentation”. IEEE Transactions on Intelligent Vehicles.

[13] F Souza, C Premebida, R Araujo (2022). “High-order conditional mutual information maximization for dealing with high order dependencies in feature selection”. Pattern Recognition, Elsevier.

[12] G Melotti, C Premebida, JJ Bird, DR Faria, N Goncalves (2022). “Reducing Overconfidence Predictions in Autonomous Driving Perception”. IEEE Access.

[11] T. Barros, P. Conde, G. Goncalves, C. Premebida, M. Monteiro, C.S.S. Ferreira, U.J. Nunes (2022). “Multispectral vineyard segmentation: A deep learning comparison study”. Computers and Electronics in Agriculture.

[10] JE Naranjo, F Jimenez, R Castineira, M Gil, C Premebida, P Serra, A Valejo, F Nashashibi, C Magalhaes (2021). “Cross-Border Interoperability for Cooperative, Connected, and Automated Driving”. IEEE Intelligent Transportation Systems Magazine.

[9] JJ Bird, CM Barnes, C Premebida, A Ekart, DR Faria (2020). “Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach”. PLoS One 15 (10).

[8] A Asvadi, L Garrote, C Premebida, P Peixoto, UJC Nunes (2017). “Multimodal vehicle detection: fusing 3D-LIDAR and color camera data”. Pattern Recognition Letters, Elsevier.

[7] C. Premebida, D. Faria, U. Nunes (2016). “Dynamic Bayesian Network for semantic place classification in mobile robotics”. Autonomous Robots (AURO), Springer.

[6] A Asvadi, C. Premebida, P. Peixoto, U. Nunes (2016). “3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes”. Robotics and Autonomous Systems (RAS), Elsevier.

[5] R. Pascoal, V. Santos, C. Premebida, U. Nunes (2014). “Simultaneous Segmentation and Superquadrics Fitting in Laser-Range Data”. IEEE Transactions on Vehicular Technology. [Code].

[4] D. Olmeda, C. Premebida, U. Nunes, J.M. Armingol, A. de la Escalera (2013). “Pedestrian detection in far infrared images”. Journal of Integrated Computer-Aided Engineering; IOS Press. Vol. 20, Number 4/2013, pp. 347-360.

[3] C. Premebida, U. Nunes (2013). “Fusing LIDAR, camera and semantic information: a context-based approach for pedestrian detection”. Int. Journal of Robotics Research, IJRR, March 2013 32: 371-384.

[2] Oswaldo Ludwig, Urbano Nunes, Bernardete Ribeiro, Cristiano Premebida (2012). “Improving the Generalization Capacity of Cascade Classifiers”. IEEE Transactions on Cybernetics, vol.PP, no.99, pp.1,12.

[1] C. Premebida, O. Ludwig and U. Nunes (2009). “LIDAR and vision-based pedestrian detection system”. Journal of Field Robotics, Wiley. Volume 26 Issue 9, pp. 696-711. [Dataset].

[5] JSS Junior, J Mendes, F Souza, C Premebida (2024). "Explainable Deep Fuzzy Systems Applied to Sulfur Recovery Unit.". Book: Machine Learning and Granular Computing: A Synergistic Design Environment, Springer.

[4] C. Premebida, G. Melotti, A. Asvadi (2019). "RGB-D object classification for autonomous driving perception". Book: RGB-D Image Analysis and Processing, Springer.

[3] D.R. Faria, C.Premebida, L.J. Manso, E.P. Ribeiro, P. Nunez (2018). "Multimodal Bayesian Network for Artificial Perception". Book: Bayesian Networks, Intech.

[2] C. Premebida, R. Ambrus, ZC. Marton (2018). "Intelligent robotic perception systems". Book: Applications of Mobile Robots, Intech.

[1] C. Premebida, F. A. de Souza, D. R. Faria (2017). "Dynamic Bayesian Network for time dependent classification problems in robotics". Book: Bayesian Inference, Intech.

[...] IP Gomes, C Premebida, DF Wolf (2023). “Interaction-aware maneuver prediction for autonomous vehicles using interaction graphs”. In: IEEE Intelligent Vehicles Symposium (IV) (Best Paper Award).

[40] JSS Junior, J Mendes, R Araujo, JR Paulo, C Premebida (2021). “Novelty Detection for Iterative Learning of MIMO Fuzzy Systems”. In: IEEE International Conference on Industrial Informatics (INDIN) (Best Paper Award).

[39] W Lu, D Zhao, C Premebida, WH Chen, D Tian (2021). “Semantic Feature Mining for 3D Object Classification and Segmentations”. In: Proc. of the IEEE Conference on Robotics and Automation (ICRA).

[38] Jordan J. Bird, Diego Faria, Cristiano Premebida, Aniko Ekart, George Vogiatzis (2020). “Look and Listen: A Multi-Modality Late Fusion Approach to Scene Classification for Autonomous Machines”. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).

[37] Jack N. C. Hayton, Tiago Barros, Cristiano Premebida, Matthew J. Coombes and Urbano J. Nunes (2020). “CNN-based Human Detection Using a 3D LiDAR onboard a UAV”. In: Proc. of the IEEE 20th International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[36] Gledson Melotti, Cristiano Premebida, Nuno Goncalves (2020). “Multimodal Deep-Learning for Object Recognition Combining Camera and LIDAR Data”. In: Proc. of the IEEE 20th International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[35] Jordan Bird, Diego R. Faria, Cristiano Premebida, Aniko Ekart, Pedro P.S. Ayrosa (2020). “Overcoming Data Scarcity in Speaker Identification: Dataset Augmentation with Synthetic MFCCs via Character-level RNN”. In: Proc. of the IEEE 20th International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[34] L. Garrote, M. Torres, T. Barros, J. Perdiz, C. Premebida, U.J. Nunes (2019). “Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System”. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019).

[33] T. Barros da Silva , L. Artur da Silva Garrote, Ricardo Pereira, C. Premebida, U. J. Nunes (2019). “Improving Localization by Learning Pole-like Landmarks Using a Semi-supervised Approach”. In: Proc. of the ROBOT'19: Iberian Robotics Conference, Porto.

[32] J. Pereira, C. Premebida, A. Asvadi, F. Cannata, L. Garrote, U.J. Nunes (2019). “Test and Evaluation of Connected and Autonomous Vehicles in Real-world Scenarios”. In: Workshop (CAD), Proc. of the IEEE Intelligent Vehicles Symposisum (IV 2019).

[31] L. Garrote, C. Premebida, D. Silva, U. J. Nunes (2018). “HMAPs - Hybrid Height-Voxel Maps for Environment Representation”. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid.

[30] G. Melotti, C. Premebida, N. Goncalves, U.J. Nunes, D. Faria (2018). “Multimodal CNN pedestrian classification: a study on combining Lidar and camera data”. In: 21th IEEE Int. Conference on Intelligent Transportation Systems (ITSC), USA.

[29] G. Melotti, A. Asvadi, C. Premebida (2018). “CNN-LIDAR pedestrian classification: combining range and reflectance data”. In: Proc. of the 20th IEEE International Conference on Vehicular Electronics and Safety (ICVES'2018), 2nd Int. Workshop on: Connected, Automated and Autonomous Vehicles (CA2V), Madrid.

[28] C. Premebida, P. Serra, A. Asvadi, A. Valejo, L. Moura (2018). “Cooperative ITS Challenges - AUTOCITS Pilot in Lisbon”. In: Proc. of the IEEE 87th Vehicular Technology Conference (VTC2018-Spring), Workshop on: Connected, Automated and Autonomous Vehicles (CA2V), Porto.

[27] C. Premebida, P. Serra, A. Asvadi, A. Valejo, R. Fonseca, R. Costa, L. Moura, C. Magalhaes (2018). “AUTOCITS Pilot in Lisbon: perspectives, challenges and approaches”. In: Proc. of the VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Funchal-Madeira.

[26] R. Castineira, J.E. Naranjo, M. Gil, F. Jimenez, P. Serra, A. Valejo, A. Asvadi, C. Premebida, M. Y. Aboualhoule, F. Nashashibi (2018). “AUTOCITS - Regulation study for interoperability in the adoption of autonomous driving in European urban nodes”. In: Proc. of the 7th Transport Research Arena TRA 2018, Austria.

[25] A. Asvadi, L. Garrote, C. Premebida, P. Peixoto, U. J. Nunes (2017). “DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet”. In: Proc. of the IEEE ITSC 2017, Yokohama, Japan.

[24] Alireza Asvadi, Luis Garrote, Cristiano Premebida, Paulo Peixoto, Urbano Nunes (2017). “Real-Time Deep ConvNet-based Vehicle Detection Using 3D-LIDAR Reflection Intensity Data”. In: Proc. of the ROBOT'2017: Third Iberian Robotics Conference, Sevilla, Spain.

[23] Diego R. Faria, Mario Vieira, Fernanda C. C. Faria, Cristiano Premebida (2017). “Affective Facial Expressions Recognition for Human-Robot Interaction”. In: Proc. of the IEEE RO-MAN'17, Lisbon, Portugal.

[22] Joao Paulo, Luis Garrote, Alireza Asvadi, Cristiano Premebida, Paulo Peixoto (2017). “Short-Range Gait Pattern Analysis for Potential Applications on Assistive Robotics”. In: Proc. of the IEEE RO-MAN'17, Lisbon, Portugal.

[21] Diego R. Faria, Fernanda C. C. Faria, Cristiano Premebida (2017). “Towards Multimodal Affective Expressions: Merging Facial Expressions and Body Motion into Emotion”. In: IEEE RO-MAN'17 Workshop on Artificial Perception, Machine Learning and Datasets for Human-Robot Interaction (ARMADA 2017), Lisbon, Portugal, pp.16-20.

[20] L. Garrote, J. Rosa, J. Paulo, C. Premebida, P. Peixoto, U. Nunes (2017). “3D Point Cloud Downsampling for 2D Indoor Scene Modelling in Mobile Robotics”. In: Proc. of the IEEE 17th International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[19] J. Paulo, L. Garrote, C. Premebida, A. Asvadi, D. Almeida, A. Lopes, P. Peixoto (2017). “An innovative robotic walker for mobility assistance and lower limbs rehabilitation”. In: Proc. of the IEEE 5th Portuguese Meeting on Bioengineering (ENBENG).

[18] R. Loureiro, A. Lopes, C. Carona, D. Almeida, F. Faria, L. Garrote, C. Premebida, U. Nunes (2017). “ISRRobotHead: Robotic head with LCD-based emotional expressiveness”. In: Proc. of the IEEE 5th Portuguese Meeting on Bioengineering (ENBENG).

[17] C. Premebida, L. Garrote, A. Asvadi, A. Pedro Ribeiro, U. Nunes (2016). “High-resolution LIDAR-based Depth Mapping using Bilateral Filter”. In: Proc. of the IEEE ITSC 2016, Rio de Janeiro, Brazil.

[16] C. Premebida, Diego R. Faria, Francisco A. de Souza, and Urbano Nunes (2015). “Applying Probabilistic Mixture Models to Semantic Place Classification in Mobile Robotics”. In: Proc. of the IEEE/RSJ IROS 2015, Hamburg, Germany.

[15] C. Premebida, Joao Sousa, Luis Garrote, and Urbano Nunes (2015). “Polar-grid representation and Kriging-based 2.5D interpolation for urban environment modelling”. In: Proc. of the IEEE ITSC 2015, Canary Islands, Spain.

[14] Diego R. Faria, Mario Vieira, C. Premebida, and U. Nunes (2015). “Probabilistic Human Daily Activity Recognition towards Robot-assisted Living”. In: Proc. of the IEEE RO-MAN'15, Kobe, Japan.

[13] R. Fernandes, C. Premebida, P. Peixoto, D. Wolf, and U. Nunes (2014). “Road Detection Using High Resolution LIDAR”. In: Proc. of the IEEE Vehicle Power and Propulsion Conference, IEEE-VPPC 2014, Coimbra, Portugal.

[12] C. Premebida, J. Carreira, J. Batista and U. Nunes (2014). “Pedestrian Detection Combining RGB and Dense LIDAR Data”. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS 2014, Chicago, USA. [Code].

[11] Diego R. Faria, C. Premebida and Urbano Nunes (2014). “A Probalistic Approach for Human Everyday Activities Recognition using Body Motion from RGB-D Images”. In: Proc. of the IEEE Int. Symposium on Robot and Human Interactive Communication, RO-MAN'14, Edinburgh, UK (Finalist for Kazuo Tanie Best Paper Award).

[10] Luis Garrote, C. Premebida, Marco Silva and Urbano Nunes (2014). “An RRT-based Navigation Approach for Mobile Robots and Automated Vehicles”. In: Proc. of the IEEE Int. Conf. on Industrial Informatics, INDIN 2014, Brasil.

[9] M. Antunes, J.P. Barreto, C. Premebida, U. Nunes (2012). “Can Stereo Vision Replace a Laser Rangefinder?”. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS 2012, Portugal.

[8] O. Ludwig, C. Premebida, U. Nunes, and R. Araujo (2011). “Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection”. In: Proc. of the IEEE ITSC 2011, Washington-DC, USA.

[7] C. Premebida, O. Ludwig, M. Silva and U. Nunes (2010). “A Cascade Classifier applied in Pedestrian Detection using Laser and Image-based Features”. In: Proc. of the IEEE ITSC 2010, Madeira, Portugal.

[6] C. Premebida, O. Ludwig, J. Matsuura and U. Nunes (2009). “Exploring sensor fusion schemes for pedestrian detection in urban scenarios”. In: Proc. IEEE/RSJ Workshop on: Safe Navigation in Open and Dynamic Environmentts held at the IROS2009, St. Louis, USA.[Dataset].

[5] C. Premebida, O. Ludwig and U. Nunes (2009). “Exploiting LIDAR-based Features on Pedestrian Detection in Urban Scenarios”. In: Proc. of the IEEE ITSC 2009, USA. [Dataset].

[4] C. Premebida, G. Monteiro, U. Nunes and P. Peixoto (2007). “A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking”. In: Proc. of the IEEE ITSC 2007, pp- 1044 - 1049, Seattle, USA.

[3] G. Monteiro, C. Premebida, P. Peixoto and U. Nunes (2006). “Tracking and Classification of Dynamic Obstacles Using Laser Ranger Finder and Vision”. In: Proc. IEEE/RSJ Workshop on: Safe Navigation in Open and Dynamic Environmentts held at the IROS2006, Beijing, China.

[2] C. Premebida and U. Nunes (2006). “A Multi-Target Tracking and GMM-Classifier for Intelligent Vehicles”. In: Proc. of the IEEE ITSC 2006, pp- 313 - 318, Toronto, Canada.

[1] C. Premebida and U. Nunes (2005). “Segmentation and Geometric Primitives Extraction from 2D Laser Range Data for Mobile Robot Applications”. In: Proc. 5rd National Festival of Robotics Scientific Meeting (ROBOTICA), pp- 17-25, Coimbra, Portugal.