65 citations · 236 across the 24 of their papers we have counts for
24 papers
H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
Mubashara Rehman, Niki Martinel, Michele Avanzo +2
Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in cancer patients with high-dens…
ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
Mubashara Rehman, Niki Martinel, Michele Avanzo +2
Artifacts in kilo-Voltage CT (kVCT) imaging degrade image quality, impacting clinical decisions. We propose a deep learning framework for metal artifact reduction (MAR) and domain…
Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement
Niki Martinel, Rita Pucci
We present a novel dual-stream architecture that achieves state-of-the-art underwater image enhancement by explicitly integrating the Jaffe-McGlamery physical model with capsule cl…
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
Nasar Iqbal, Niki Martinel
Recent advances in convolutional neural networks (CNNs) and transformer-based methods have improved anomaly detection and localization, but challenges persist in precisely localizi…
SkelMamba: A State Space Model for Efficient Skeleton Action Recognition of Neurological Disorders
Niki Martinel, Mariano Serrao, Christian Micheloni
We introduce a novel state-space model (SSM)-based framework for skeleton-based human action recognition, with an anatomically-guided architecture that improves state-of-the-art pe…
MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning
Belén Serrano-Antón, Mubashara Rehman, Niki Martinel +5
For the planning of radiotherapy treatments for head and neck cancers, Computed Tomography (CT) scans of the patients are typically employed. However, in patients with head and nec…
CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement
Rita Pucci, Niki Martinel
Unmanned underwater image analysis for marine monitoring faces two key challenges: (i) degraded image quality due to light attenuation and (ii) hardware storage constraints limitin…
NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results
Zheng Chen, Zongwei Wu, Eduard Zamfir +85
This paper reviews the NTIRE 2024 challenge on image super-resolution (4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating…
Tracking Skiers from the Top to the Bottom
Matteo Dunnhofer, Luca Sordi, Niki Martinel +1
Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' p…
UW-ProCCaps: UnderWater Progressive Colourisation with Capsules
Rita Pucci, Niki Martinel
Underwater images are fundamental for studying and understanding the status of marine life. We focus on reducing the memory space required for image storage while the memory space…
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization
Rita Pucci, Christian Micheloni, Niki Martinel
The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured. Deep neur…
TUCaN: Progressively Teaching Colourisation to Capsules
Rita Pucci, Niki Martinel
Automatic image colourisation is the computer vision research path that studies how to colourise greyscale images (for restoration). Deep learning techniques improved image colouri…
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in ma…
Collaboration among Image and Object Level Features for Image Colourisation
Rita Pucci, Christian Micheloni, Niki Martinel
Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attack…
Is It a Plausible Colour? UCapsNet for Image Colourisation
Rita Pucci, Christian Micheloni, Gian Luca Foresti +1
Human beings can imagine the colours of a grayscale image with no particular effort thanks to their ability of semantic feature extraction. Can an autonomous system achieve that? C…
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking is the problem of predicting a target object's state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been…
Tracking-by-Trackers with a Distilled and Reinforced Model
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we…
An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks
Enkhtogtokh Togootogtokh, Christian Micheloni, Gian Luca Foresti +1
Modern Unmanned Aerial Vehicles equipped with state of the art artificial intelligence (AI) technologies are opening to a wide plethora of novel and interesting applications. While…
Video-Based Convolutional Attention for Person Re-Identification
Marco Zamprogno, Marco Passon, Niki Martinel +5
In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping…
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator
Matteo Dunnhofer, Niki Martinel, Gian Luca Foresti +1
In the last decade many different algorithms have been proposed to track a generic object in videos. Their execution on recent large-scale video datasets can produce a great amount…
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding
Giuseppe Lisanti, Niki Martinel, Alberto Del Bimbo +1
Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addresse…
The UMCD Dataset
Danilo Avola, Gian Luca Foresti, Niki Martinel +2
In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks. In particul…
Wide-Slice Residual Networks for Food Recognition
Niki Martinel, Gian Luca Foresti, Christian Micheloni
Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition alg…
Temporal Model Adaptation for Person Re-Identification
Niki Martinel, Abir Das, Christian Micheloni +1
Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn…