The Machine Intelligence Laboratory (formerly known as
the Speech, Vision and Robotics group) was founded by the late
Professor Frank Fallside in the early 1970's, when the main interests
were in speech processing and control applications. In the mid 1980's,
the laboratory developed a strong interest in the theory and
application of neural networks and this led to a widening of the
laboratory's research to include vision and robotics. Medical imaging
followed in the mid 1990's. Today, the guiding principle of all
research in the laboratory is that a well-designed engineering system
must be based on a sound mathematical model. In this regard, neural
networks represent just one of a wide range of applicable
techniques. Others include stochastic processes such as hidden Markov
models, Bayesian inference, invariant transformations in 3D geometry,
computational geometry, Wiener and Kalman filtering, classification
and regression trees, and genetic algorithms.
A full list of research projects is given
elsewhere , but the principle areas of interest
are as follows:
- Neural networks, pattern recognition and machine learning, including multi-layer perceptrons, radial basis functions, and recurrent networks.
- Signal processing, non-stationary time-series analysis, speech coding and compression.
- Speech recognition using both neural networks and hidden Markov Models. This includes large vocabulary recognition, recognition in noise, speaker adaptation and word spotting.
- Language processing including N-grams, stochastic context-free grammars, grammatical inference, dictionary construction.
- Image processing and object recognition, including 3-D reconstruction from 2-D images, image segmentation, and face recognition.
- Visual navigation of mobile robots and task level and sensor-based robot control within an unstructured environment.
- Aspects of robot assembly including path planning, hand-eye coordination and quality inspection using computer vision, man-machine interfaces using visual gestures.
- Aspects of medical imaging, including the acquisition, visualisation, registration and segmentation of 3D ultrasound images for medical diagnosis.
- Risk analysis in various aspects of health care.
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