Neural network-based detection of virtual environment anomalies


Autoria(s): Nantes, Alfredo; Brown, Ross A.; Maire, Frederic D.
Data(s)

24/08/2012

Resumo

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.

Identificador

http://eprints.qut.edu.au/53505/

Publicador

Springer-Verlag London Limited

Relação

DOI:10.1007/s00521-012-1132-x

Nantes, Alfredo, Brown, Ross A., & Maire, Frederic D. (2012) Neural network-based detection of virtual environment anomalies. Neural Computing & Applications, 21, pp. 1-18.

Fonte

School of Electrical Engineering & Computer Science; School of Information Systems; Science & Engineering Faculty; School of Urban Development; Smart Transport Research Centre

Palavras-Chave #090000 ENGINEERING #Anomaly detection #Visual correctness #3D virtual environment #Virtual environment testing #Computer game testing
Tipo

Journal Article