Hostname: page-component-7dd5485656-frp75 Total loading time: 0 Render date: 2025-10-30T04:37:29.087Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling and simulation with augmented reality

Published online by Cambridge University Press:  15 April 2004

Khaled Hussain  and
Varol Kaptan
Khaled Hussain
Affiliation:
School of Computer Science, University of Central Florida Orlando, Florida 32816, USA; khaled@cs.ucf.edu.
Varol Kaptan
Affiliation:
Department of Electrical & Electronic Engineering, Imperial College, London SW7 2BT, UK; v.kaptan@imperial.ac.uk.
Get access

Abstract

In applications such as airport operations, militarysimulations, and medical simulations, conductingsimulations in accurate and realistic settings that are represented byreal video imaging sequences becomes essential. This paper surveys recent work that enablesvisually realistic model constructions and the simulation of syntheticobjects which are inserted in video sequences, and illustrates how synthetic objects canconduct intelligent behavior within a visual augmented reality.

Information

Type
Research Article
Information
RAIRO - Operations Research , Volume 38 , Issue 2: Advances in modelling of complex systems , April 2004 , pp. 89 - 103
Copyright
© EDP Sciences, 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Arkin, R.C., Motor Schema-Based Mobile Robot Navigation. Int. J. Robot. Res. 8 (1989) 92-112. CrossRef
R.C. Arkin, Behavior-Based Robotics. The MIT Press, Cambridge, Massachusetts (1998).
Bajura, M. and Neumann, U., Dynamic registration correction in video-based reality systems. IEEE Comput. Graph. Appl. 15 (1995) 52-60. CrossRef
T. Balch, Behavioral Diversity in Learning Robot Teams. Ph.D. Thesis, Georgia Institute of Technology (1998).
E. Gelenbe, Reseaux neuronaux aleatoires stables. C.R. Acad. Sci. II 310 (1990) 177-180.
R. Brooks, A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA-2 14-23 (1986).
R. Brooks, Cambrian Intelligence: The Early History of The New AI. The MIT Press, Cambridge, MA (1999).
Cramer, C., Gelenbe, E. and Bakircioglu, H., Low bit rate video compression with neural networks and temporal subsampling. Proc. IEEE 84 (1996) 1529-1543. CrossRef
Cramer, C. and Gelenbe, E., Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences. IEEE J. Selected Areas in Communications 18 (2000) 150–167. CrossRef
Feng, Y. and Gelenbe, E., Adaptive object tracking and video compression. Netw. Inform. Syst. J. 1 (1999) 371-400.
B. Foss, E. Gelenbe, K. Hussain, N. Lobo and H. Bahr, Simulation driven virtual objects in real scenes. Proc. ITSEC 2000, Orlando, FL (Nov. 2000).
E. Gelenbe, Reseaux stochastiques ouverts avec clients negatifs et positifs, et reseaux neuronaux. C.R. Acad. Sci. Paris II 309 (1989) 979-982.
Gelenbe, E., Random neural networks with positive and negative signals and product form solution. Neural Comput. 1 (1989) 502-510. CrossRef
Gelenbe, E., Stable random neural networks. Neural Comput. 2 (1990) 239-247. CrossRef
Gelenbe, E., Learning in the recurrent random network. Neural Comput. 5 (1993) 154-164. CrossRef
E. Gelenbe, Modeling CGF with learning stochastic finite-state machines. Proc. 8th Conference on Computer Generated Forces, Orlando, May 11–13, 113-116.
E. Gelenbe, Simulation with goal-oriented agents. in EUROSIM 2001. Delft University of Technology Delft, Netherlands, 26–30 June (2001).
E. Gelenbe, Applications of spiked recurrent stochastic networks in 13th International Conference on Artificial Neural Networks & 10th International Conference on Neural Information Processing, 26–29 June (2003).
E. Gelenbe, Spiked random neural networks, product forms, learning and approximation in Conference on Analytical and Stochastic Modeling Techniques and Applications, European Simulation Multi-conference, Nottingham, UK, 9–11 June (2003).
Gelenbe, E., Cramer, C., Sungur, M. and Gelenbe, P., Traffic and video quality in adaptive neural compression. Multimedia Systems 4 (1996) 357-369. CrossRef
Gelenbe, E., Feng, T. and Krishnan, K.R.R., Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc. IEEE 84 (1996) 1488-1496. CrossRef
Gelenbe, E. and Fourneau, J.M., Random neural networks with multiple classes of signals. Neural Comput. 11 (1999) 953-963. CrossRef
Gelenbe, E. and Hussain, K., Learning in the multiple class random neural network. IEEE Trans. on Neural Networks 13 (2002) 1257-1267. CrossRef
E. Gelenbe, K. Hussain and V. Kaptan, Realistic simulation of cooperating robots, in Proc. CTS'03 (International Symposium on Collaborative Technologies and Systems), WMC'03 Society for Computer Simulation, Orlando, 19–23 January (2003), 151-156.
E. Gelenbe, V. Kaptan and K. Hussain, Simulating Autonomous Agents in Augmented Reality. Submitted for publication.
E. Gelenbe, E. Şeref and Z. Xu, Discrete event simulation using goal oriented learning agents, AI, Simulation & Planning in High Autonomy Systems, SCS, Tucson, Arizona, March 6–8 (2000).
Gelenbe, E., E. Şeref and Z. Xu, Simulation with learning agents. Proc. IEEE 89 (2001) 148-157. CrossRef
S.W. Lawson, Augmented reality for underground pipe inspection and maintenance, in SPIE Conference on Telemanipulator and Telepresence Technologies, Boston, Massachusetts, 3524 (1998) 98-104.
Mataric, M., Reinforcement learning in the multi-robot domain. Autonomous Robots 4 (1997) 73-83. CrossRef
J.P. Mellor, Enhanced reality visualization in a surgical environment. M.S. Thesis, Department of Electrical Engineering, MIT (January 1995).
N. Ono and K. Fukumoto, Multi-agent reinforcement learning: A modular approach in Proc. of the 2nd Int. Conf. on Multi-Agent Systems. AAAI Press (1996) 252-258.
N. Ono and K. Fukumoto, A modular approach to multi-agent reinforcement learning, edited by Gerhard Weiss in Springer-Verlag, Distributed Artificial Intelligence Meets Machine Learning, (1997) 167.
D.C. Pottinger, Implementing Coordinated Movement. Game Developer Magazine (1999).
J.H. Reif and H. Wang, Social Potential Fields: A Distributed Behavioral Control for Autonomous Robots, in International Workshop on Algorithmic Foundations of Robotics (WAFR), edited by A.K. Peters, Wellesley, MA (1998) 431-459.
Reynolds, C.W., Flocks, Herds, and Schools: A Distributed Behavioral Model. Comput. Graph. 21 (1987) 25–34. CrossRef
C.W. Reynolds, Steering Behaviors for Autonomous Characters. Game Developers Conference (1999).
Steels, L., The Artificial Life Roots of Artificial Intelligence. Artificial Life 1 (1993) 75-110. CrossRef
M. Tan, Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents, in International Conference on Machine Learning (1993) 330-337.