Pedro Conde

According to our database, Pedro Conde authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

PointNetPGAP-SLC: A 3D LiDAR-Based Place Recognition Approach With Segment-Level Consistency Training for Mobile Robots in Horticulture.

Tiago Barros, Luís Garrote, Pedro Conde, Matthew J. Coombes, Cunjia Liu, Cristiano Premebida, Urbano J. Nunes

IEEE Robotics Autom. Lett., November, 2024

PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture.

Tiago Barros, Luís Garrote, Pedro Conde, Matthew Coombes, C. Liu, Cristiano Premebida, Urbano J. Nunes

CoRR, 2024

2023

Probabilistic Approach for Road-Users Detection.

Gledson Melotti, Weihao Lu, Pedro Conde, Dezong Zhao, Alireza Asvadi, Nuno Gonçalves, Cristiano Premebida

IEEE Trans. Intell. Transp. Syst., September, 2023

A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection.

Pedro Conde, Rui L. Lopes, Cristiano Premebida

CoRR, 2023

Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks.

Pedro Conde, Tiago Barros, Rui L. Lopes, Cristiano Premebida, Urbano J. Nunes

CoRR, 2023

ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards.

Tiago Barros, Luis Garrote, Pedro Conde, Matthew Coombes, C. Liu, Cristiano Premebida, Urbano J. Nunes

CoRR, 2023

ECE-based Deep Ensemble for Neural Network Calibration in Satellite Image Classification.

Pedro Conde, Tiago Barros, Cristiano Premebida, Urbano J. Nunes

Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions, 2023

2022

Multispectral vineyard segmentation: A deep learning comparison study.

Tiago Barros, Pedro Conde, Gil Gonçalves, Cristiano Premebida, Miguel Monteiro, Carla Sofia Santos Ferreira, Urbano J. Nunes

Comput. Electron. Agric., 2022

Adaptive-TTA: accuracy-consistent weighted test time augmentation method for the uncertainty calibration of deep learning classifiers.

Pedro Conde, Cristiano Premebida

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

Multispectral Vineyard Segmentation: A Deep Learning approach.

Tiago Barros, Pedro Conde, Gil Gonçalves, Cristiano Premebida, Miguel Monteiro, Carla Sofia Santos Ferreira, Urbano J. Nunes

CoRR, 2021