most citedExploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

2 citations · 2 across the 1 of their papers we have counts for

collaborators

3 papers

cs.CV2025

Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation

Diogo Mendonça, Tiago Barros, Cristiano Premebida +1

Autonomous-driving perception systems require robust Multi-Object Tracking (MOT) to operate reliably in dynamic environments. MOT maintains consistent object identities across fram…

cs.RO2024

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…

cs.CV2024★ 2 cited

Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

Ricardo Pereira, Luís Garrote, Tiago Barros +2

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Desp…