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Computing Science and Maths Seminars, 2020/2021

Spring 2020 image

Seminars will take place via Microsoft Teams, with a meeting link to be shared via the seminar-announce emails. Unless otherwise stated, from 15.00 to 16.00 on Friday afternoons during semester time, followed by informal discussions.

If you would like to give a seminar to the department in future or if you need more information,  
please contact the seminar organisers, Dr. Sandy Brownlee (sbr@cs.stir.ac.uk) and Dr. Wen-shin Lee (wen-shin.lee@stir.ac.uk)

Autumn 2020

Date Speaker Title/Abstract
Friday
18 September


Friday
25 September
Dr. Mohamed Elawady, CSM, University of Stirling Reflection Symmetry Detection in 2D Images

Abstract:
Symmetry is a fundamental principle of visual perception to feel the equally distributed weights within foreground objects inside an image. It is used as a significant visual feature through various computer vision applications (i.e. object detection and segmentation), plus as an important composition measure in the art domain (i.e. aesthetic analysis). The development of symmetry detection has been improving rapidly since last century. In this work, the main objective is detecting reflection symmetry inside real-world images in a global scale. This method wins a recent symmetry competition worldwide in single and multiple cases. In summary, the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes.

Bio:
Mohamed Elawady is a new appointed university lecturer in data science, Stirling university. He was a lead computer vision researcher at a parisian startup: Qopius. He has studied in different European universities: PhD in computer vision at Hubert Curien laboratory, Lyon university [France] (2014-2019). European masters in vision and robotics (VIBOT) with Erasmus Mundus scholarship at Burgundy university [France], Girona university [Spain] and Heriot-Watt university [UK] (2012-2014). In addition, he is the winner of 2D reflection symmetry competitions (among participants) in ICCV’17 workshop: Detecting symmetry in the wild.
Friday
2 October


Friday
9 October


Friday
16 October


Friday
23 October


Friday
30 October
Reading Week

Friday
6 November
No seminar this week

Friday
13 November
Dr Vahid Akbari, CSM, University of Stirling Machine Learning and Big Data Meet Synthetic Aperture Radar (SAR)

Change detection, classification, and object detection are very important for many applications of remotely sensed data from space- and air-borne instruments. Especially synthetic aperture (SAR) data are useful due to their all-weather capabilities. My major Focus in the last 12 years has been mainly on the algorithm development in machine learning and statistical modeling in interferometric and polarimetric SAR from satellite, and aircraft sensors for studies of land deformation, land cover classification, and change detection, as well as, marine target detection and characterization in polar regions and forest monitoring. In this presentation, I will give some of my research outputs highlighting where SAR might meet machine learning and big data. There will be seven major areas in my presentation:
  • Big Interferometric SAR data for land subsidence monitoring.
  • Segmentation of multi-channel SAR data based on a novel unsupervised machine learning algorithm and advanced statistical modeling.
  • How to visualize texture in matrix-variate data? Post-classification change detection in bing SAR data using machine learning technique.
  • Change detection in matrix-variate PolSAR data sets.
  • Machine learning in marine applications and challenges.
  • Machine learning in forest age characterization and classification.
  • Multitemporal analysis of big SAR data for forest clear cut detection.
Speaker Bio:Dr. Vahid Akbari received the M.Sc. degree in remote sensing (summa cum laude) from the University of Tehran, Iran, in 2009 and the Ph.D. degree in physics major in earth observation from the UiT The Arctic University of Norway, Tromsø, Norway, in 2013. He continued his research in radar remote sensing as a Postdoctoral Research Fellow with the Department of Physics and Technology, UiT The Arctic University of Norway and Norwegian Instiute of Bioeconomy Research. He has been a Visiting Scientist with the Signal Processing and Telecommunications Laboratory of the Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Italy, in 2011, and a Visiting Researcher with German Geoscience Center (GFZ), Potsdom, Germany, in 2008. He has been an Assistant Professor of Remote Sensing with the University of Tehran in 2015. He has been Adviser for seven M.Sc. and two Ph.D. students. He is currently a Research Fellow with Division of Computing Science and Mathematics of the University of Stirling. His research interests include development of methods in machine learning, pattern recognition and image processing to extract information of land and ocean with radar remote sensing data.
Friday
20 November
Dr. Carles Barril, Department of Mathematics, Universitat Autonoma de Barcelona Basic reproduction number for an age of infection epidemic model

Abstract:
In this talk we show how to define the basic reproduction number (the so-called R0) in continuously structured populations and, in particular, in an epidemic in which individuals are structured by the age of infection and distributed between asymptomatic and symptomatic. We consider an example of application to data of the crucial moment of the epidemic of the Covid-19 in Spain.
Friday
27 November
Virtual Graduation for Postgraduates

Friday
4 December
Prof. Bruce Graham, CSM, University of Stirling Two pathway signalling processing in the brain

Cortical pyramidal cells (PCs) have evolved to process two separate streams of driving input and to process them separately before they combine to generate an output from the cell. Thus we can regard such neurons as 2-point processors. This is quite unlike typical neurons in artificial neural networks, which treat all inputs equally and thus function as single point processors. In this talk we will consider the what, how and why of these inputs: what the two streams are, how they are combined in PCs and what it means for information processing in the cortex.

Previous Seminar Series

2020:  Spring   Autumn
2019:  Spring   Autumn
2018:  Spring   Autumn
2017:  Spring   Autumn
2016:  Spring   Autumn
2015:  Spring   Autumn
2014:  Spring   Autumn
2013:  Spring   Autumn
2012:  Spring   Autumn
2011:  Spring   Autumn
2010:  Spring   Autumn
2009:  Spring   Autumn
2008:  Spring   Autumn
2007:  Spring   Autumn
2006:  Spring   Autumn
2005:  Spring   Autumn
2004:  Spring   Autumn
2003:  Spring   Autumn
2002:  Spring   Autumn
2001:  Spring   Autumn
2000:  Spring   Autumn
1999:  Spring   Autumn
1998:  Spring   Autumn
1997:  Spring   Autumn
1996:  Autumn
 

Top image: Evolution of sounds by crowd-sourcing, from Brownlee, A. E. I., Kim, S-J., Wan, S-H., Chan, S. & Lawson, J. A. Crowd-Sourcing the Sounds of Places with a Web-Based Evolutionary Algorithm. Companion Proc. of the Genetic and Evolutionary Computation COnference 2019, Prague, Czech Republic, pp 131-132. DOI:10.1145/3319619.3322028


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