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LAVA - Learning for Adaptable Visual Assistants

The key problem that must be solved in order to build cognitive vision systems is the robust, efficient and learnable categorisation and interpretation of large numbers of objects, scenes and events, in real settings. LAVA will create technologies enabling such systems and an understanding of the systems - and user-level aspects of their applications, via a novel alliance between statistical learning theory, computer vision and cognitive science experts. For practical computational efficiency and robustness, we shall devise methods for goal-directed visual attention and the integration of multiple asynchronous visual cues. These results will be embodied in two integrated systems: one will employ vision for information retrieval in a mobile setting; the other will derive symbolic representations from video sequences, enabling a wide range of ambient intelligence scenarios.

Project Website http://www.l-a-v-a.org
Project Coordinator's email Chris Dance < chris.dance@xrce.xerox.com >
Project Summary
(64kb)

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