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

Automated in-core image generation from video to aid visual inspection of nuclear power plant cores
Murray Paul, West Graeme, Marshall Stephen, McArthur Stephen
Nuclear Engineering and Design Vol 300, pp. 57-66 (2016)
Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applications
Qiao Tong, Ren Jinchang, Sun Meijun, Zheng Jiangbin, Marshall Stephen
International Journal of Remote Sensing Vol 35, pp. 7316-7337 (2014)
https://doi.org/10.1080/01431161.2014.968682
Novel multivariate vector quantization for effective compression of hyperspectral imagery
Li Xiaohui, Ren Jinchang, Zhao Chunhui, Qiao Tong, Marshall Stephen
Optics Communications Vol 332, pp. 192-200 (2014)
https://doi.org/10.1016/j.optcom.2014.07.011
Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
Ren Jinchang, Zabalza Jaime, Marshall Stephen, Zheng Jiangbin
IEEE Signal Processing Magazine Vol 31, pp. 149-154 (2014)
https://doi.org/10.1109/MSP.2014.2312071
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
Zabalza Jaime, Ren Jinchang, Yang Mingqiang, Zhang Yi, Wang Jun, Marshall Stephen, Han Junwei
ISPRS Journal of Photogrammetry and Remote Sensing Vol 93, pp. 112-122 (2014)
https://doi.org/10.1016/j.isprsjprs.2014.04.006
Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging
Zabalza Jaime, Ren Jinchang, Ren Jie, Liu Zhe, Marshall Stephen
Applied Optics Vol 53, pp. 4440-4449 (2014)
https://doi.org/10.1364/AO.53.004440

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

Faculty Robotics and Automation Users Group Discussion
Participant
10/10/2017
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
Irish Machine Vision and Image Processing (IMVIP 2014)
Keynote/plenary speaker
27/8/2014
Transforming Institutions: 21st Century Undergraduate STEM Education Conference
Participant
23/10/2014

more professional activities

Projects

Multitask Deep Learning from Images for Clinical Decision Support
Ren, Jinchang (Principal Investigator) Marshall, Stephen (Co-investigator)
01-Jan-2018 - 30-Jan-2023
BBSRC Pathfinder: A new tool for bioimaging based on super-resolution Raman microscopy
Marshall, Stephen (Principal Investigator)
23-Jan-2018 - 22-Jan-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)
23-Jan-2018 - 22-Jan-2019
NNL Gamechanger (student Support)
Marshall, Stephen (Principal Investigator)
14-Jan-2018 - 31-Jan-2018
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.
20-Jan-2017 - 19-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

more projects

Address

Electronic and Electrical Engineering
Royal College Building

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