Neural network-based detection of virtual environment anomalies
The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.
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|Item Type:||Journal Article|
|Additional Information:||Springer Online First publication|
|Keywords:||Anomaly detection, Visual correctness, 3D virtual environment, Virtual environment testing, Computer game testing|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > School of Urban Development
Current > Research Centres > Smart Transport Research Centre
|Deposited On:||06 Sep 2012 22:26|
|Last Modified:||12 Jun 2013 15:09|
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