Search Results for author: Spyridon Thermos

Found 13 papers, 10 papers with code

vMFNet: Compositionality Meets Domain-generalised Segmentation

1 code implementation29 Jun 2022 Xiao Liu, Spyridon Thermos, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris

Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image.

Anatomy Image Segmentation +2

A Low-Cost & Real-Time Motion Capture System

no code implementations CVPR 2022 Anargyros Chatzitofis, Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption.

Denoising

Learning Disentangled Representations in the Imaging Domain

1 code implementation26 Aug 2021 Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris

Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.

Representation Learning

Controllable cardiac synthesis via disentangled anatomy arithmetic

1 code implementation4 Jul 2021 Spyridon Thermos, Xiao Liu, Alison O'Neil, Sotirios A. Tsaftaris

Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e. g. MRI), plausible new cardiac images are created with the target characteristics.

Anatomy

Measuring the Biases and Effectiveness of Content-Style Disentanglement

2 code implementations27 Aug 2020 Xiao Liu, Spyridon Thermos, Gabriele Valvano, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris

In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.

Disentanglement Image-to-Image Translation

Disentangled Representations for Domain-generalized Cardiac Segmentation

1 code implementation26 Aug 2020 Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris

Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains.

Anatomy Cardiac Segmentation +4

A Deep Learning Approach to Object Affordance Segmentation

no code implementations18 Apr 2020 Spyridon Thermos, Petros Daras, Gerasimos Potamianos

In particular, we design an autoencoder that is trained using ground-truth labels of only the last frame of the sequence, and is able to infer pixel-wise affordance labels in both videos and static images.

Human-Object Interaction Detection object-detection +2

Deep Soft Procrustes for Markerless Volumetric Sensor Alignment

2 code implementations23 Mar 2020 Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras

This is accomplished by a soft, differentiable procrustes analysis that regularizes the segmentation and achieves higher extrinsic calibration performance in expanded sensor placement configurations, while being unrestricted by the number of sensors of the volumetric capture system.

Pose Estimation

Self-Supervised Deep Depth Denoising

1 code implementation ICCV 2019 Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras

Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference.

3D Reconstruction Denoising

Deep Affordance-grounded Sensorimotor Object Recognition

no code implementations CVPR 2017 Spyridon Thermos, Georgios Th. Papadopoulos, Petros Daras, Gerasimos Potamianos

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them.

Object Recognition

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