Charalambos (Charis) Poullis - Immersive & Creative Technologies Lab

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Charalambos (Charis) Poullis

Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery

building footprint Featured

Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery

The paper 'Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery' by Yeshwanth Kumar Adimoolam, Charalambos Poullis, and Melinos Averkiou has been accepted for publication in IEEE/CVF WACV 2025. TL;DR: This work introduces Pix2Poly, an attention-based, end-to-end trainable, and differentiable

From data to action in flood forecasting leveraging graph neural networks and digital twin visualization

forecasting Featured

From data to action in flood forecasting leveraging graph neural networks and digital twin visualization

The paper "From data to action in flood forecasting leveraging graph neural networks and digital twin visualization" by Naghmeh Shafiee Roudbari, Shubham Rajeev Punekar, Zachary Patterson, Ursula Eicker, and Charalambos Poullis has been accepted for publication in Scientific Reports. TL;DR: Firstly, we present the graph neural network

Transductive meta‑learning with enhanced feature ensemble for few‑shot semantic segmentation

Transductive meta‑learning with enhanced feature ensemble for few‑shot semantic segmentation

The paper "Transductive Meta-Learning with Enhanced Feature Ensemble for Few-shot Semantic Segmentation" by Amin Karimi and Charalambos Poullis has been accepted for publication in Scientific Reports. TL;DR: We propose two-pass end-to-end method for few-shot semantic segmentation. The approach leverages an ensemble of visual features learned from pretrained

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

Our paper TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting has been published as a conference paper at the International Conference on Machine Learning and Applications (ICMLA), 2023. The work is co-authored by Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, and Ursula Eicker.

Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning: A Case Study on Object Tracking Tasks

Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning: A Case Study on Object Tracking Tasks

Our paper Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning: A Case Study on Object Tracking Tasks has been published as a conference paper at the 18th International Symposium on Visual Computing (ISVC), 2023. The work is co-authored by Jatin Katyal and Charalambos Poullis.

Tracking and Identification of Ice Hockey Players

Tracking and Identification of Ice Hockey Players

Our paper Tracking and Identification of Ice Hockey Players has been published as a conference paper at the International Conference on Computer Vision Systems (ICVS), 2023. The work is co-authored by Qiao Chen and Charalambos Poullis.

Enabling saccadic redirection through real-time saccade prediction, Computer Animation and Virtual Worlds 2023

Enabling saccadic redirection through real-time saccade prediction, Computer Animation and Virtual Worlds 2023

Our paper Enabling saccadic redirection through real-time saccade prediction has been published as a journal paper in Computer Animation and Virtual World 2023. The work is co-authored by Yashas Joshi and Charalambos Poullis. Link to the paper: https://onlinelibrary.wiley.com/doi/10.1002/cav.2167 Gameplay: Here's

Bodhiswatta Chatterjee awarded FRQNT Doctoral Research Scholarship

Bodhiswatta Chatterjee awarded FRQNT Doctoral Research Scholarship

Congratulations to Bodhiswatta Chatterjee for being awarded the FRQNT Doctoral Research Scholarship! The FRQNT Doctoral Research Scholarship is a highly competitive and prestigious award, reflecting the excellent quality of his research and academic potential. As a graduate from the Immersive and Creative Technologies Lab, Bodhi has already demonstrated strong skills

Tractable Large-Scale Deep Reinforcement Learning, CVIU 2023

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Tractable Large-Scale Deep Reinforcement Learning, CVIU 2023

Our paper Tractable Large-Scale Deep Reinforcement Learning has been accepted for publication as a journal paper by Computer Vision and Image Understanding. The work is co-authored by Nima Sarang and Charalambos Poullis. A preprint is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4121080 Abstract: Reinforcement learning

Analysis of Error Rate in Hierarchical Menu Selection in Immersive Augmented Reality, SPIE AR|VR|MR 2023

augmented reality

Analysis of Error Rate in Hierarchical Menu Selection in Immersive Augmented Reality, SPIE AR|VR|MR 2023

Our paper Analysis of Error Rate in Hierarchical Menu Selection in Immersive Augmented Reality has been accepted for publication at SPIE AR|VR|MR, 2023. The work is co-authored by Majid Pourmemar and Charalambos Poullis. Abstract: The emergence of new immersive AR/VR headsets recently resulted in major improvements in

Motion Estimation for Large Displacements and Deformations, Scientific Reports 2022

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Motion Estimation for Large Displacements and Deformations, Scientific Reports 2022

Our paper Motion Estimation for Large Displacements and Deformations has been accepted for publication as a journal paper by Scientific Reports. The work is co-authored by Qiao Chen and Charalambos Poullis. The camera-ready paper is now available: https://www.nature.com/articles/s41598-022-21987-7 Abstract: Large displacement optical flow is an

End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes

End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes

Our paper "End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes" has been accepted as a conference paper at the IEEE International Conference on Signal Processing, Sensors, and Intelligent Systems (SPSIS), 2022. The paper is co-authored by Qiao Chen, and Charalambos Poullis. Abstract: Image-based 3D reconstruction is one of the most

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Our paper "Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction" has been accepted as a conference paper at the IEEE Computational Intelligence In Vehicles And Transportation Systems (CIVTS), 2022. The paper is co-authored by Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker,

Unsupervised Structure-Consistent Image-to-Image Translation, ISVC 2022

Unsupervised Structure-Consistent Image-to-Image Translation, ISVC 2022

Our work on "Unsupervised Structure-Consistent Image-to-Image Translation " has been accepted as a conference paper at the 17th International Symposium on Visual Computing (ISVC), 2022. The paper is co-authored by Shima Shahfar and Charalambos Poullis. Abstract: The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation.

Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze, HSI 2022

augmented reality Featured

Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze, HSI 2022

The paper titled Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze authored by Majid Pourmemar, Yashas Joshi and Charalambos Poullis will be presented at the 15th Conference on Human System Interaction, 2022. Abstract: There are currently limited guidelines on designing user interfaces (UI)

Adaptive Memory Management for Video Object Segmentation, CRV 2022

video object segmentation

Adaptive Memory Management for Video Object Segmentation, CRV 2022

Our work on "Adaptive Memory Management for Video Object Segmentation" has been accepted as a conference paper at 19th Conference on Robots and Vision (CRV), 2022. The paper is co-authored by Ali Pourganjalikhan, Charalambos Poullis. Abstract: Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks

Managing real world and virtual motion

virtual reality

Managing real world and virtual motion

A US Patent was issued for the technology presented in the IEEE Access journal and the ACM SIGGRAPH poster on redirected walking by Yashas Joshi and Charalambos Poullis. Abstract Navigation of a virtual environment (VE) can mean navigating a VE that is spatially larger than the available Physical Tracked Space