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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. 

Publications

A fast hyperspectral hit-or-miss transform with integrated projection-based dimensionality reduction
Macfarlane Fraser, Murray Paul, Marshall Stephen, White Henry
Hyperspectral Imaging Applications (HSI) 2018, (2018)
A colour hit-or-miss transform based on a rank ordered distance measure
Macfarlane Fraser, Murray Paul, Marshall Stephen, Perret Benjamin, Evans Adrian, White Henry
26th European Signal Processing Conference, (2018)
FPGA implementation of a memory-efficient Hough Parameter Space for the detection of lines
Northcote David, Crockett Louise H., Murray Paul
2018 IEEE International Symposium on Circuits and Systems (ISCAS), (2018)
http://dx.doi.org/10.1109/ISCAS.2018.8351115
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)
http://dx.doi.org/10.1016/j.matdes.2017.12.049
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)
Design of a 2D sparse array transducer for integration into an ergonomic transcranial ultrasound system
Li Xiaotong, Gachagan Anthony, Dziewierz Jerzy, O'Leary Richard, Murray Paul
2017 IEEE International Ultrasonics Symposium, IUS 2017, (2017)
http://dx.doi.org/10.1109/ULTSYM.2017.8091863

more publications

Teaching

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.

Projects

Spectral Data Analysis for Spectral Unmixing and Feature Detection
Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator)
Period 01-Apr-2018 - 11-Jun-2018
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)
Period 23-Apr-2018 - 22-Oct-2019
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)
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) 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.
Period 01-Jul-2016 - 31-Dec-2017

more projects

Address

Electronic and Electrical Engineering
Royal College Building

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