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Dr Paul Murray

Strathclyde Chancellor'S Fellow

Electronic and Electrical Engineering

Personal statement

I am a Research Associate at the University of Strathclyde working in the area of image processing. I work on a number of research an industrial projects where I apply my image processing skills to address a wide range of challenges in many application areas. My activities range from analysing microscopic biological images to processing images captured during visual inspection of nuclear power plants and more.

My research interests can be summarised as: signal and image processing, hyperspectral imaging, image stitching, object detection and tracking, mathematical morphology and the hit-or-miss-transform.

In general, I am interesting in extending and creating new image processing techniques to adress real world and industrially relevant challenges. For example, the outcomes of my research conducted during my PhD have been used in various biological research labs in the UK and in the USA. More recently, upon completing an intensive two year research project, I delivered the oucomes of my research in the form of novel image stitching software to a leading energy company. 


New methods for automatic quantification of microstructural features using digital image processing
Campbell Andrew, Murray Paul, Yakushina Evgenia, Marshall Stephen, Ion William
Materials and Design Vol 141, pp. 395-406, (2018)
Towards extracting 3-D structural representations of AGR core fuel channels from 2-D in-core inspection videos
Law Kristofer, West Graeme, Murray Paul, Lynch Chris
International Symposium on Future I&C for Nuclear Power Plants (ISOFIC 2017), pp. 1-10, (2017)
3-D advanced gas-cooled nuclear reactor fuel channel reconstruction using structure-from-motion
Law Kristofer, West Graeme, Murray Paul, Lynch Chris
10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, (2017)
Removal of specular reflections from image sequences using feature correspondences
M. Z. Abbas Shah Syed., Marshall Stephen, Murray Paul
Machine Vision and Applications Vol 28, pp. 409-420, (2017)
Use of near- and mid-IR hyperspectral imaging for paint identification, as an aid for artwork authentication
Polak A., Stothard D.J.M., Kelman T., Murray P., Marshall S.
43rd Freiburg Infrared Colloquium, pp. 1-2, (2017)
Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication
Polak Adam, Kelman Timothy, Murray Paul, Marshall Stephen, Stothard David J. M., Eastaugh Nicholas, Eastaugh Francis
Journal of Cultural Heritage Vol 26, pp. 1-11, (2017)

more publications


I support teaching image and video processing in an MSc and MEng class dedicated to this subject. I also teach image processing as part a Vertically Integrated Project (VIP). VIP is a new initiative in teaching recently adopted at Strathclyde which allows teams of undergraduates to work on state-of-the-art research projects throughout their university career. I also take a Small Group Tutorial which provides additional academic and general support to first year students to help them adapt to university life.


Industrial CASE Account - University of Strathclyde 2017 | Macfarlane, Fraser
Marshall, Stephen (Principal Investigator) Murray, Paul (Co-investigator) Macfarlane, Fraser (Research Co-investigator)
Period 01-Oct-2017 - 01-Oct-2021
EPSRC Centre for Doctoral Training in Medical Devices and Health Technologies | Governo, Mark
Graham, Duncan (Principal Investigator) Murray, Paul (Co-investigator) Governo, Mark (Research Co-investigator)
Period 01-Oct-2016 - 01-Oct-2020
Doctoral Training Partnership (DTP - University of Strathclyde) | Northcote, David
Crockett, Louise Helen (Principal Investigator) Murray, Paul (Co-investigator) Northcote, David (Research Co-investigator)
Period 01-Oct-2015 - 01-Apr-2019
16AGRITECHCAT5: Feasibility of a Hyper Spectral Crop Camera (HCC) for agriculture optimisation
Marshall, Stephen (Principal Investigator) Murray, Paul (Research Co-investigator)
Farmers and horticulturists face varying difficulties that require experience and knowledge of their fields and crops, gained over many years. These difficulties include, but are not limited to: uneven growth/yield of their fields; inexact and estimated fertiliser application; uneven irrigation and local variations in pests/diseases/weeds. Additionally, the optimum harvest timing is still speculated and often inexact. Faced with numerous variables, farmers cannot avoid high variations in costs and crop yields from year to year. Tools to assist farmers to optimise e.g. fertiliser & water applications or early detection of disease will provide a useful diagnostic and management capability for optimum control of crop growth. Currently, solutions for these challenges do exist, however, current systems are large, heavy, not portable and as such are not readily deployable. They are also prohibitively expensive - typically £10,000 - £150,000 each - and are generally only suitable for use in airborne or satellite imaging applications or laboratory analysis. In effect, the current solutions available for the aforementioned agricultural challenges are limited to large scale farming and/ or high value crops. In these expensive systems, a spectrometer scan or image of the crop is taken at visible and/or infrared wavelengths with analysis showing spectral image signature changes relating to crop growth conditions. The signatures of interest varies from plant to plant and from cause to cause. The colour of a crop (visible and IR) also changes as it approaches maturity, with spectrometer scans providing scientific information for informed management decisions in relation to crop hydration, fertiliser application, disease progression and harvesting. Hyperspectral Imaging (HSI) can capture these changes: HSI systems capture a large number of images of the scene, each at a different wavelength within some range determined by the sensor technology, to produce a so called hyperspecal data cube in which each pixel in the spatial domain contains a spectral profile of the object observed. For our application, this spectral information can be analysed to make decisions about the diagnostics/management of challenges in maximising crop yield. The proposed Hyperspectral Crop Camera (HCC) will be: low-cost, compact portable, simple in operation and robust. A camera housing will contain the sensor, battery and electronics to produce one small simple lightweight device. This device would be suitable for handheld use or potentially mountable in a low cost drone for local airborne analysis. HSI technology in farming and agriculture which can cost anything from £10k - £150k. Application of HCC can allow a farmer and/ or agriculturists to: - Save water by providing optimised or localised irrigation - Timely identify areas of pests/diseases/weeds for early intervention - Optimise use of fertiliser - Determine optimum harvest time and help increase crop yield - Improve evenness of crop yield across field area - Reduced man hours, manually surveying fields etc - Reduce need for technical agronomy training/knowledge.
Period 01-Jul-2016 - 31-Dec-2017
Industrial Case Account (2008-2012) | Murray, Paul
Marshall, Stephen (Principal Investigator) Soraghan, John (Co-investigator) Murray, Paul (Research Co-investigator)
Period 01-Oct-2008 - 02-May-2012
Automatic Rice Seed Inspection Using Hyper-Spectral Imaging (Newton Fund)
Tachtatzis, Christos (Principal Investigator) Harle, David (Co-investigator) Marshall, Stephen (Co-investigator) Murray, Paul (Co-investigator)
Period 15-Nov-2015 - 14-Feb-2017

more projects


Electronic and Electrical Engineering
Royal College Building

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