Machine Vision Laboratory an overview

The Machine Vision Laboratory is part of the Department of Engineering Design and Mathematics in the Faculty of Environment and Technology.

Our objective

The objective of the Machine Vision Research Group has been to establish an internationally recognised centre of excellence in fundamental and applied research. The team has aimed to achieve this by:

Undertaking original, cutting edge research
Winning research and consultancy contracts from both commercial and publicly funded bodies world-wide
Demonstrating excellence in research through international publication
Networking with other such centres both in UK and abroad

The Machine Vision Laboratory was established in 1999 and has developed rapidly in terms of its size, experience, and the reputation of its team members. The laboratory supports RAE level research activities and researchled teaching in machine vision. We have pioneered the application of an innovative technique known as Photometric Stereo (PS) in industry, medicine and defence/security. Our laboratory has also developed expertise in the application of general three-dimensional techniques for computer vision, all of which offer significant advantages over existing two-dimensional methods. These special advantages can be of vital importance where surface reflectance and/or shape data are central to the application. Another distinctive feature of our laboratory is our significant emphasis on both hardware design and algorithm development. On a number of projects we were able to collaborate with industrial partners to develop working prototypes.

We have developed a network of close collaborative academic and industrial partnerships throughout the UK, Europe and the USA. The UWE Machine Vision Laboratory is now recognized as one of only 3 UK centres with expertise in PS. Currently the Machine Vision Laboratory is staffed by some 10 full-time researchers.

We are actively engaged in networking and new bid preparation, particularly in evolving medical and security areas. This includes new opportunities for 3D imaging in improved diagnostics and human behaviour analysis.

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