41 citations · 58 across the 16 of their papers we have counts for
16 papers · 1 filter
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
Mohammad Mohammadi, Sina Honari, Stavros Tsogkas +6
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal d…
RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution
Ali Mosleh, Faraz Ali, Fengjia Zhang +4
Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due…
Probabilistic Directed Distance Fields for Ray-Based Shape Representations
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
In modern computer vision, the optimal representation of 3D shape continues to be task-dependent. One fundamental operation applied to such representations is differentiable render…
Efficient Flow-Guided Multi-frame De-fencing
Stavros Tsogkas, Fengjia Zhang, Allan Jepson +1
Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid.…
Representing 3D Shapes with Probabilistic Directed Distance Fields
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks.…
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
A complete representation of 3D objects requires characterizing the space of deformations in an interpretable manner, from articulations of a single instance to changes in shape ac…
Cycle-Consistent Generative Rendering for 2D-3D Modality Translation
Tristan Aumentado-Armstrong, Alex Levinshtein, Stavros Tsogkas +2
For humans, visual understanding is inherently generative: given a 3D shape, we can postulate how it would look in the world; given a 2D image, we can infer the 3D structure that l…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas +2
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervi…
Geometric Disentanglement for Generative Latent Shape Models
Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson +1
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun t…
DeepFlux for Skeletons in the Wild
Yukang Wang, Yongchao Xu, Stavros Tsogkas +3
Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recen…
AMAT: Medial Axis Transform for Natural Images
Stavros Tsogkas, Sven Dickinson
We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem. We make the followin…
Prior-based Coregistration and Cosegmentation
Mahsa Shakeri, Enzo Ferrante, Stavros Tsogkas +4
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map o…
Sub-cortical brain structure segmentation using F-CNN's
Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante +4
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-…
Deep Learning for Semantic Part Segmentation with High-Level Guidance
S. Tsogkas, I. Kokkinos, G. Papandreou +1
In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this…