Charalambos Poullis

Charalambos Poullis

Latest — May 9, 2023

Enabling Saccadic Redirection Through Real-time Saccade Prediction

The paper Enabling Saccadic Redirection Through Real-time Saccade Prediction by Yashas Joshi and Charalambos Poullis has been accepted for publication in the Computer Games and Virtual Worlds journal. A provisional patent has been issued for this technology (Licensing contact: Axelys). A short video presentation of the technique is shown below.

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Data leakage and Duplication in Benchmark Dataset

In a recent comprehensive analysis, we have uncovered substantial data leakage, duplication, and annotation discrepancies in the popular CrowdAI Mapping Challenge benchmark dataset, which has been extensively utilized for developing semantic segmentation and footprint extraction algorithms of buildings from satellite imagery. This revelation may call into question the validity of

Tractable Large-Scale Deep Reinforcement Learning

The paper Tractable Large-Scale Deep Reinforcement Learning by Nima Sarang and Charalambos Poullis has been accepted for publication in the Computer Vision and Image Understanding journal. The code is publicly available at https://github.com/nsarang/road-extraction-rl. Some results are shown below. TL;DR: We frame the problem of road

Analysis of error rate in hierarchical menu selection in immersive augmented reality

Our paper Analysis of error rate in hierarchical menu selection in immersive augmented reality has been accepted for publication in SPIE AR|VR|MR. The work is co-authored by Majid Pourmemar and Charalambos Poullis. The paper is now available: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12449/124491L/Analysis-of-error-rate-in-hierarchical-menu-selection-in-immersive/10.1117/12.

Managing real-world and virtual motion

A US Patent was published for the technology presented in the IEEE Access journal and the ACM SIGGRAPH poster on redirected walking by Yashas Joshi and Charalambos Poullis. TL;DR: Navigating large-scale virtual environments in room-scale physical spaces using natural locomotion is problematic since users will most certainly encounter obstacles,

Motion Estimation for Large Displacements and Deformations

The preprint of our paper Motion Estimation for Large Displacements and Deformations -published in Scientific Reports- is available on arxiv.org. 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 The work was featured in LeDevoir (article)

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

The preprint of our paper Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction is available on arxiv.org. The work is co-authored by Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, and Charalambos Poullis. TL;DR: We present a sequence-to-sequence architecture to extract the

Unsupervised Structure-Consistent Image-to-Image Translation

The preprint of our paper Unsupervised Structure-Consistent Image-to-Image Translation is available on arxiv.org. The work is co-authored by Shima Shahfar and Charalambos Poullis. TL;DR: We present a structure-consistent image-to-image translation technique based on the swapping autoencoder. We introduce an auxiliary module which forces the generator to learn to

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

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. TL;DR: User studies are expensive, time-consuming, and primarily based on

SaccadeNet: Towards Real-time Saccade Prediction for Virtual Reality Infinite Walking

The preprint of our paper SaccadeNet: Towards Real-time Saccade Prediction for Virtual Reality Infinite Walking is available on arxiv.org. The work is co-authored by Yashas Joshi and Charalambos Poullis. TL;DR: We propose a deep neural network for predicting saccades in VR applications of moderate cognitive workload. During saccades,