24 citations · 50 across the 4 of their papers we have counts for
5 papers
Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data
Mariona Carós, Ariadna Just, Santi Seguí +1
Airborne LiDAR systems have the capability to capture the Earth's surface by generating extensive point cloud data comprised of points mainly defined by 3D coordinates. However, la…
Object Segmentation of Cluttered Airborne LiDAR Point Clouds
Mariona Caros, Ariadna Just, Santi Segui +1
Airborne topographic LiDAR is an active remote sensing technology that emits near-infrared light to map objects on the Earth's surface. Derived products of LiDAR are suitable to se…
Uncertainty Gated Network for Land Cover Segmentation
Guillem Pascual, Santi Seguí, Jordi Vitrià
The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep…
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Santi Seguí, Michal Drozdzal, Guillem Pascual +4
The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help ph…
Learning to count with deep object features
Santi Seguí, Oriol Pujol, Jordi Vitrià
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is t…