Dr Paul Murray

Strathclyde Chancellor's Fellow

Electronic and Electrical Engineering

Personal statement

I am a Lecturer 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.

My research interests can be summarised as: signal and image processing, hyperspectral imaging, machine learning, 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. 

Publications

Comparative study of PCA and LDA for rice seeds quality inspection
Fabiyi Samson Damilola, Vu Hai, Tachtatzis Christos, Murray Paul, Harle David, Dao Trung-Kien, Andonovic Ivan, Ren Jinchang, Marshall Stephen
IEEE Africon 2019 (2019)
Automatic events extraction in pre-stack seismic data based on edge detection in slant-stacked peak amplitude profiles
Zhao Jing, Ren Jinchang, Gao Jinghuai, Tschannerl Julius, Murray Paul, Wang Daxing
Journal of Petroleum Science and Engineering Vol 178, pp. 459-466 (2019)
https://doi.org/10.1016/j.petrol.2019.03.062
Using vertically integrated projects to embed research-based education for sustainable development in undergraduate curricula
Strachan Scott Munro, Marshall Stephen, Murray Paul, Coyle Edward J, Sonnenberg-Klein Julie
International Journal of Sustainability in Higher Education (2019)
https://doi.org/10.1108/IJSHE-10-2018-0198
Object detection and classification in aerial hyperspectral imagery using a multivariate hit-or-miss transform
Macfarlane Fraser, Murray Paul, Marshall Stephen, White Henry
SPIE Defense and Commercial Sensing 2019 (2019)
https://doi.org/10.1117/12.2518103
Automated analysis of AGR fuel channel inspection videos
Devereux Michael, Murray Paul, West Graeme
2019 Innovation Showcase: Nuclear Asset Management and Industrial Informatics (2019)
Use of hyperspectral imaging for cake moisture and hardness prediction
Polak Adam, Coutts Fraser Kenneth, Murray Paul, Marshall Stephen
IET Image Processing Vol 13, pp. 1152-1160 (2019)
https://doi.org/10.1049/iet-ipr.2018.5106

more publications

Teaching

I am teaching Digital Electronics and 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 have also taught Masters level courses in Image and Video processing as well as looking after a Small Group Tutorial which provides additional academic and general support to first year students to help them adapt to university life.

Professional activities

Environmental Association of Universities and Colleges - Annual Conference 2019
Invited speaker
19/5/2019

more professional activities

Projects

STRAthclyde Diversity in Data LinkagE (STRADDLE) Doctoral Training Centre
Barry, Sarah (Co-investigator) Kavanagh, Kimberley (Co-investigator) Megiddo, Itamar (Co-investigator) Murray, Paul (Co-investigator) Rattray, Nicholas (Co-investigator)
The focus of the STRAthclyde Diversity in Data LinkagE (STRADDLE) DTC is to develop a centre of excellence in the linkage and analysis of data across disciplines. With an initial focus on health and healthcare delivery, STRADDLE will deliver linkage and innovative analysis of data from the molecular to population level, to achieve new insights and to maximize the value of health research data. There are 3 fully funded, 3.5 year PhD studentships are available to highly motivated UK and EU students. Successful applicants will be trained as the next generation of data scientists and develop skills and know how to deliver on the integration of diverse clinical data types – from molecules to man to populations.
01-Jan-2019 - 31-Jan-2023
ANRC -04 Advanced Image Processing Techniques for In-core Inspections
West, Graeme (Principal Investigator) Murray, Paul (Co-investigator)
01-Jan-2018 - 31-Jan-2021
CMSIN-II (CEOI Resubmission) Compact Multi-Spectral Imager for Nanosatellites II
Oi, Daniel (Principal Investigator) Griffin, Paul (Co-investigator) Jeffers, John (Co-investigator) Macdonald, Malcolm (Co-investigator) Marshall, Stephen (Co-investigator) Murray, Paul (Co-investigator) Lowe, Christopher (Research Co-investigator)
23-Jan-2018 - 22-Jan-2019
Industrial CASE Account - University of Strathclyde 2017 | Macfarlane, Fraser
Marshall, Stephen (Principal Investigator) Murray, Paul (Co-investigator) Macfarlane, Fraser (Research Co-investigator)
01-Jan-2017 - 01-Jan-2021
16AGRITECHCAT5: Feasibility of a Hyper Spectral Crop Camera (HCC) for agriculture optimisation
Marshall, Stephen (Principal Investigator) Murray, Paul (Research Co-investigator) Macfarlane, Fraser (Researcher)
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.
01-Jan-2016 - 31-Jan-2017
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)
15-Jan-2015 - 14-Jan-2017

more projects

Address

Electronic and Electrical Engineering
Royal College Building

Location Map

View University of Strathclyde in a larger map