78 citations · 207 across the 11 of their papers we have counts for
11 papers
Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery
Leonardo Saraceni, Ionut Marian Motoi, Daniele Nardi +1
In precision agriculture, the scarcity of labeled data and significant covariate shifts pose unique challenges for training machine learning models. This scarcity is particularly p…
Synthetic Data Generation for Anomaly Detection on Table Grapes
Ionut Marian Motoi, Valerio Belli, Alberto Carpineto +2
Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. Computer vision and robotics are increasingly em…
Can Robots "Taste" Grapes? Estimating SSC with Simple RGB Sensors
Thomas Alessandro Ciarfuglia, Ionut Marian Motoi, Leonardo Saraceni +1
In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SS…
Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery
Ionut M. Motoi, Leonardo Saraceni, Daniele Nardi +1
Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification…
AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture
Leonardo Saraceni, Ionut M. Motoi, Daniele Nardi +1
The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object. It is a challen…
Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data
Thomas A. Ciarfuglia, Ionut M. Motoi, Leonardo Saraceni +3
Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications.…
The Role of the Input in Natural Language Video Description
Silvia Cascianelli, Gabriele Costante, Alessandro Devo +3
Natural Language Video Description (NLVD) has recently received strong interest in the Computer Vision, Natural Language Processing (NLP), Multimedia, and Autonomous Robotics commu…
Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks - Application on Synthetic Aperture Radar Images
Gabriele Costante, Thomas A. Ciarfuglia, Filippo Biondi
Synthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surfa…
J-MOD: Joint Monocular Obstacle Detection and Depth Estimation
Michele Mancini, Gabriele Costante, Paolo Valigi +1
In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approach…
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation
Gabriele Costante, Thomas A. Ciarfuglia
This work proposes a novel deep network architecture to solve the camera Ego-Motion estimation problem. A motion estimation network generally learns features similar to Optical Flo…
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks
Michele Mancini, Gabriele Costante, Paolo Valigi +1
Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved…