Interaction with virtual agents – Comparison of the participants’ experience between an IVR and a semi-IVR system
Marios Kyriakou, Xueni Pan, Yiorgos Chrysanthou
Presented at: IEEE Virtual Reality (VR) conference. IEEE. pp. 217–218, Mar 2015
Toward Energy-Aware Balancing of Mobile Graphics
Efstathios Stavrakis, Marios Polychronis, Nectarios Pelekanos, Alessandro Artusi, Panayiotis Hadjichristodoulou, Yiorgos Chrysanthou
Presented at: Mobile Devices and Multimedia: Enabling Technologies, Algorithms and Applications, SPIE 9411, Mar 2015
A Data‐Driven Framework for Visual Crowd Analysis
Panayiotis Charalambous, Ioannis Karamouzas, Stephen J. Guy, Yiorgos Chrysantho
Computer Graphics Forum, vol. 33, no. 7, pp. 41-50, Oct 2014
Presented at: Pacific Graphics 2014
We present a novel approach for analyzing the quality of multi-agent crowd simulation algorithms. Our approach is data-driven, taking as input a set of user-defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state-of-the-art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per-agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real-time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely-used methods, including a simulation from a commercial game.
Automatic Emotion Recognition Based on Body Movement Analysis: A Survey
Haris Zacharatos, Christos Gatzoulis, Yiorgos Chrysanthou
IEEE Computer Graphics and Applications 34.6 (2014), pp. 35–45, Sep 2014
The PAG Crowd: A Graph Based Approach for Efficient Data‐Driven Crowd Simulation.
Panayiotis Charalambous, Yiorgos Chrysanthou.
Computer Graphics Forum, vol. 33, no. 8, pp. 95-108, Jun 2014
We present a data‐driven method for the real‐time synthesis of believable steering behaviours for virtual crowds. The proposed method interlinks the input examples into a structure we call the perception‐action graph (PAG) which can be used at run‐time to efficiently synthesize believable virtual crowds. A virtual character's state is encoded using a temporal representation, the Temporal Perception Pattern (TPP). The graph nodes store groups of similar TPPs whereas edges connecting the nodes store actions (trajectories) that were partially responsible for the transformation between the TPPs. The proposed method is being tested on various scenarios using different input data and compared against a nearest neighbours approach which is commonly employed in other data‐driven crowd simulation systems. The results show up to an order of magnitude speed‐up with similar or better simulation quality.
Cypriot Intangible Cultural Heritage: Digitizing Folk Dances
Andreas Aristidou, Efstathios Stavrakis, Yiorgos Chrysanthou
Cyprus Computer Society journal, Issue 25, pages 42-49, Apr 2014
We aim to preserve the Cypriot folk dance heritage, creating a state-of-the-art publicly accessible digital archive of folk dances. Our dance library, apart from the rare video materials that are commonly used to document dance performances, utilises three dimensional motion capture technologies to record and archive high quality motion data of expert dancers.
Learning Through Multi-Touch Interfaces in Museum Exhibits: an Empirical Investigation
Panagiotis Zaharias, Despina Michael, Yiorgos Chrysanthou
Educational Technology & Society 16.3 (2013), pp. 374–384, Sep 2013
Selective Local Tone Mapping
Alessandro Artusi, Ahmet Oguz Akyuz, Benjamin Roch, Despina Michael, Yiorgos Chrysanthou, Alan Chalmers
Presented at: IEEE International Conference on Image Processing (ICIP). IEEE. 2013, pp. 2309– 2313, Jan 2013