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Professor Stephen Marshall

Electronic and Electrical Engineering

Personal statement

I am a Professor in the Department of Electronic and Electrical Engineering where I am also Deputy Head of Department and Director of the Hyperspectral Imaging (HSI) Centre. The Centre carries out pure and applied research into all aspects of HSI and currently has 11 staff and researchers.

I am also the University's academic lead for the Verically Integrated Project (VIP) Program which brings together students, researchers and staff from across disciplines and academic years to tackle challenging problems.

 

Publications

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
Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement
Yan Yijun, Ren Jinchang, Sun Genyun, Zhao Huimin, Han Junwei, Li Xuelong, Marshall Stephen, Zhan Jin
Pattern Recognition, (2018)
http://dx.doi.org/10.1016/j.patcog.2018.02.004
Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos
Yan Yijun, Ren Jinchang, Zhao Huimin, Sun Genyun, Wang Zheng, Zheng Jiangbin, Marshall Stephen, Soraghan John
Cognitive Computation, (2017)
http://dx.doi.org/10.1007/s12559-017-9529-6
Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification
Cao Faxian, Yang Zhijing, Ren Jinchang, Ling Wing-Kuen, Zhao Huimin, Marshall Stephen
Remote Sensing Vol 9, (2017)
http://dx.doi.org/10.3390/rs9121255
Error model of misalignment error in a radial 3D scanner
Mathur Neha, Summan Rahul, Dobie Gordon, West Graeme, Marshall Stephen
IEEE SENSORS 2017, (2017)
Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injury
Md Noor Siti Salwa, Michael Kaleena, Marshall Stephen, Ren Jinchang
Sensors (Switzerland) Vol 17, (2017)
http://dx.doi.org/10.3390/s17112644

more publications

Teaching

I currently teach EE581/981 Video and Image Processing to level 5 and Masters students. I have previously taught electronic design, CAD and Computer Vision.

Research interests

  • Digital image processing and computer vision,
  • Hyperspectral Imaging,
  • non-linear image processing,
  • mathematical morphology,
  • digital image coding,
  • medical image processing,
  • genomic signal processing,
  • genetic algorithms and FPGAs.

Professional activities

IET Image Processing (Journal)
Associate Editor
1/2/2016
Hyperspectral Imaging 2016
Organiser
1/1/2016
23rd European Signal Processing Conference, 2015 (EUSIPCO 2015)
Participant
1/9/2015
Transforming Institutions: 21st Century Undergraduate STEM Education Conference
Participant
23/10/2014
Hyperspectral Imaging and Applications Conference (HSI 2014)
Organiser
15/10/2014
Irish Machine Vision and Image Processing (IMVIP 2014)
Keynote/plenary speaker
27/8/2014

more professional activities

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
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
KTP - Innovent
Ren, Jinchang (Principal Investigator) Marshall, Stephen (Co-investigator)
Period 01-May-2017 - 30-Oct-2019
Doctoral Training Partnership (DTP 2016-2017 University of Strathclyde) | McDonald, Liam
Dobie, Gordon (Principal Investigator) Marshall, Stephen (Co-investigator) McDonald, Liam (Research Co-investigator)
Period 01-Oct-2016 - 01-Oct-2019
Inversion Analysis of Surface Seismic Data for Effective Exploration of Oil and Gas Reservoirs
Ren, Jinchang (Principal Investigator) Marshall, Stephen (Co-investigator) Tarantino, Alessandro (Co-investigator) Weiss, Stephan (Co-investigator)
Period 01-Mar-2017 - 28-Feb-2019
A new tool for bioimaging based on super resolution Raman microscopy
Graham, Duncan (Principal Investigator) Faulds, Karen (Co-investigator) Marshall, Stephen (Co-investigator)
Raman microscopy is a technique which interacts laser light of a particular wavelength with a target sample resulting in this light being scattered by the sample, the changes in energy of the scattered light is then measured. These changes in energy relate to vibrations from different molecules and produce a vibrational fingerprint of the sample relating to the molecular composition. When conducted using a microscope and a stage which moves, multiple Raman spectra in 2 and 3 dimensions can be acquired to produce an image of the sample based on the intensity and the location of particular vibrations within the sample. This is referred to as a Raman map and is very often a false colour map laid on top of a standard magnified microscope image of the sample, a white light image, e.g. a heat map of intensity of say a protein vibration overlaid on the image of a cell. Conventional Raman microscopy is normally in a confocal mode which means that the highest resolution in spatial terms is half the wavelength of the excitation light so typically around 250 nm. Biological structures and processes are on a much smaller scale and this is a limitation of Raman spectroscopy. An advantage of Raman spectroscopy is that it is label free and reliant on the specific molecular vibrations from the molecules in the interrogation volume, unlike fluorescence microscopy, which is the most commonly used form of optical microscopy in life sciences. However fluorescence microscopy requires addition of a label to the sample which changes the sample composition and can affect the intrinsic biological processes of a biological system. This proposal will produce a new tool to acquire Raman maps and then process the data to enhance the spatial resolution possible from a Raman confocal microscope. We propose to generate sub 100 nm spatial resolution using this tool which will greatly transform the use of Raman spectroscopy and microscopy in the life sciences. This tool will require no addition of labels or hardware modifications to existing Raman microscope instruments.
Period 20-Nov-2017 - 19-Feb-2019

more projects

Address

Electronic and Electrical Engineering
Royal College Building

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