8 citations · 16 across the 6 of their papers we have counts for
6 papers
SceneHI: High-Resolution 3D-Consistent Scene Texturing with Controllable Illumination
Athanasios Tragakis, Marco Aversa, Daniela Ivanova +4
SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first to demonstrate that high-reso…
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution
Athanasios Tragakis, Chaitanya Kaul, Kevin J. Mitchell +3
Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) dep…
AI-Enabled sensor fusion of time of flight imaging and mmwave for concealed metal detection
Chaitanya Kaul, Kevin J. Mitchell, Khaled Kassem +6
In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor…
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
Athanasios Tragakis, Marco Aversa, Chaitanya Kaul +2
In this work, we introduce Pixelsmith, a zero-shot text-to-image generative framework to sample images at higher resolutions with a single GPU. We are the first to show that it is…
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
Athanasios Tragakis, Qianying Liu, Chaitanya Kaul +5
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We re…
The Fully Convolutional Transformer for Medical Image Segmentation
Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith +1
We propose a novel transformer model, capable of segmenting medical images of varying modalities. Challenges posed by the fine grained nature of medical image analysis mean that th…