Visualitzant Articles publicats (D-ATC) per autor "Agencia Estatal de Investigación"
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Two-Dimensional Frontier-Based Viewpoint Generation for Exploring and Mapping Underwater Environments

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

State of the Art of Underwater Active Optical 3D Scanners

Evaluating the Effect of Intensity Standardisation on Longitudinal Whole Brain Atrophy Quantification in Brain Magnetic Resonance Imaging

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information

Underwater 3D Scanner Model Using a Biaxial MEMS Mirror

Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks

Extrinsic visual–inertial calibration for motion distortion correction of underwater 3D scans

Semantic mapping for autonomous subsea intervention

Visual attention and color cues for 6d pose estimation on occluded scenarios using rgb-d data

Linewise Non-Rigid Point Cloud Registration

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis

Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry

Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data

A convolutional vision transformer for semantic segmentation of side-scan sonar data

An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images

MAM-E: Mammographic synthetic image generation with diffusion models

Underwater Pose SLAM using GMM scan matching for a mechanical profiling sonar

Sparus Docking Station: A current aware docking station system for a non-holonomic AUV

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework

Autonomous Underwater Vehicle Docking Under Realistic Assumptions Using Deep Reinforcement Learning