Computational Perception Group

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Machine Perception

Statistical models for audio and video

Theoretical understanding of learning algorithms as probabilistic inference

Machine Vision

Learning invariances from natural images for object recognition

Statistical models for images

Machine Hearing

Synthesis of audio textures for computer games and artificial environments

Source separation

Neuroscience

Auditory processing as probabilistic inference

Neural implementations of approximate inference

Machine Learning

Approximate inference for time-series

Circular statistics and time-series

Signal Processing

Unifying signal processing and machine learning

Removing signal distortions using machine learning & signal processing


If you want to find out more about the core technical material that is relevant to the Group's reseach, please see the Group's recommended reading list.

 
Best viewed with Firefox 3, Safari 4 or Explorer 8. 2009 Computational & Biological Learning Lab, Department of Engineering, University of Cambridge