Paper: Neural network-based detection of virtual environment anomalies

Just had a paper Neural network-based detection of virtual environment anomalies accepted to Neural Computing and Applications, with Alfredo Nantes and Frederic Maire.  The paper is here at QUT ePrints, and here at Springer.

Abstract 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 in 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 this work, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multi-Layer Perceptrons and Self Organizing Maps are trained to learn the normal geometric and color appearance of the objects from validated frames, and 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.

Well done Alfredo!

Ross