Dr. Neil Thacker's Profile Page

Background

My background research training was obtained in High Energy Physics on an experiment at CERN. Since then I have spent 18 years as a computer vision researcher. The first five years were spent at the Artificial Intelligence Vision Research Unit where I was involved in the inital development of the TINA vision system. This has formed a key focus for subsequent research and has cullminated in several advanced systems one of which being a stereo vision system for robotic applications. I joined the Electronic Syeysems Group in 1992 and was there for a further 5 years. While there, my research focussed on the design of VLSI chips for computer vision algorithmic support. This resulted in the fabrication of several working devices.

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My work has addressed a wide area of topics including: neural networks, stereo vision and object recognition. A strong component of this has always been the use of statistical methodology. I have quite strong views on the need for algorithmic methodologies for design and testing (see machine vision notes below). I believe that such approaches (commonly found in other scientific disciplines) could significantly improve the quantity and quality of useful results in this area.

My current position at the University of Manchester is a Senior Lecturer in Neuroimage Analysis. In this capacity I was the first person in the University of Manchester to develop an fMRI analysis system for the investigation of brain activity using MR. This work continued for several years, and included experiments to investigate the functional processes underlying object constancy, in collaboration with researchers in the University of Bangor.

I teach Advanced Machine Vision to MSc students on various courses around this University and contribute each year to the EPSRC Machine Vision Summerschool. I am actively involved with the BMVA in which I have previously held positions of publicity officer and meetings secretary.

I have now joined the steering committe for the annual Medical Image Understanding and Analysis conference.

I supervise 2 PhD students (Simon Coupe and Sashi Palaniswamy) and a researcher funded on a University Stepping Stones Award (Paul Bromiley) in the areas of medical imaging and machine vision. My research often involves assessing the basic principles on which the subjects of computer vision and image analysis are founded.

A Selection of Fundamental Questions Addressed in Recent Research

• Do we believe that a good starting point for a theory of human vision is to assume it makes the best use of the the data available?
• Does model selection (pattern recognition) actually require knowledge of the measurement errors for a meaningful solution?
• Do we have to accept that there are a multitude of ways of comparing probability density distributions, and can the Kullback-Liebler Divergence ever be used legitimately as the theoretical basis for an algorithm?
• Is Likelihood really required to be formulated in a space of uniform errors in order to remain quantitatively valid?
• Are algorithms based upon closed form solutions to sets of constraint equations apparently mathematically solid but in fact generally statistically inept?
• Are some theoretical mathematical approaches, such as Riemann geometry, incapable of supporting a valid statistical framework?
• Is it true that an algorithm based upon Bayes Inference can never be meaningfully tested?
• Do either Bayes Theory or Information Theory offer a quantitatively valid generalisation to the theory of Likelihood?
• Are estimation techniques based upon either Mutual Information or MAP simply a bad re-invention of Likelihood?

To find out answers and opinions look at the technical documents on our Tina web pages.

EPSRC Summerschool notes (Performance Characterisation)(postscript)
EPSRC Summerschool notes (transparencies)(postscript)
(PCCV Methodology Document)(pdf)
(Manjon Data)(gzipped tarfile)
Journal Publications
Conference Publications

E-Mail contact: neil.thacker (at) manchester.ac.uk