Pattern Recognition & AI

The Centre prides itself on its international reputation and leadership role in the area of pattern recognition, the key technology of multimedia signal interpretation. Its research has contributed many of the state of the art methods in various aspects of pattern recognition system design. CVSSP techniques which help to identify the most discriminative pattern attributes, are used by practitioners worldwide. The Centre's expertise in classifier design spans statistical, decision tree, neural network and support vector machine approaches. The recent innovations made at the Centre, such as the predictive validation approach to modelling pattern classes help to solve as diverse problems as target and outlier (abnormality, fault) detection. The contextual decision making methods pioneered at the Centre have found applicability in 3D object recognition, aerial image matching, and land cover classification in remote sensing. The methodology of multiple expert fusion contributed by CVSSP has helped to enhance the performance of pattern recognition systems in many application areas.

ACASVA is an example of one of the Centre's collaborative projects that combines pattern recognition, AI, audio-visual analysis and psychology.