Dr Hannah Dee

BSc, MA, PhD University of Leeds

Dr Hannah Dee

Senior Lecturer

Contact Details

Profile

Hannah has a BSc in Cognitive Science (1996), an MA in Philosophy (1998) and a PhD in Computing (2005) all from the University of Leeds. Her research areas are applied computer vision; the detection of shadows and reasoning about shadows; and student attitudes to the study of computer science. She has held postdoctoral positions in Grenoble (France), Leeds, and Kingston upon Thames. She is also a women in computing activist: she founded the BCSWomen Lovelace Colloquium, a national conference for women undergraduates in computer science, and is still deputy chair. She has been on the committee of BCSWomen since 2007.

Teaching

Module Coordinator
Lecturer
Additional Lecturer

Hannah enjoys teaching and has received several teaching awards since arriving in Aberystwyth:

  • Senior fellowship of the HEA (SFHEA), 2018
  • Student led teaching awards (SLTA), highly commended, technology enhanced learning category, 2016
  • Exemplary Course Awards (ECA), highly commended for Computer Vision, 2015
  • Exemplary Course Awards (ECA), highly commended for Client Side Graphics module, 2014
  • Student led teaching awards (SLTA), highly commended, teaching through technology category, 2013
  • Aberystwyth University Learning and Teaching Fellowship (AULTF), 2012

Research Groups

Publications

Shadow detection for mobile robots: Features, evaluation, and datasetsNewey, C., Jones, O. & Dee, H. 2017 In : Spatial Cognition and Computation.p. 1-2323 p.
Watching plants grow: A position paper on computer vision and Arabidopsis thalianaBell, J. & Dee, H. 2017 In : IET Computer Vision.11, 2, p. 113-1219 p.
Probabilistic self-localisation on a qualitative map based on occlusionsSantos, P. E., Martins, M. F., Fenelon, V., Cozman, F. G. & Dee, H. 2016 In : Journal of Experimental & Theoretical Artificial Intelligence.28, 5, p. 781-79919 p.
Visual digital humanities: using image data to derive approximate metadataDee, H., Hughes, L., Roderick, G. L. & Brown, A. D. 2016 Managing Digital Cultural Objects: Analysis, discovery and retrieval. Foster, A. & Rafferty, P. (eds.). London: Facet Publishing, p. 89-11021 p.
Special issue on computer vision and image analysis in plant phenotypingScharr, H., Dee, H., French, A. & Tsaftaris, S. A. 2016 In : Machine Vision and Applications.27, 5, p. 607-6093 p.
3D Facial Skin Texture Analysis Using Geometric DescriptorsSeck, A., Dee, H. M. & Tiddeman, B. 2014 22nd International Conference on Pattern Recognition. Stockholm: IEEE Press, p. 1126 - 11316 p. (International Conference on Pattern Recognition)
Towards Automated Classification of Seabed Substrates in Underwater VideoPugh, M., Tiddeman, B., Dee, H. M. & Hughes, P. 2014 Computer Vision for Analysis of Underwater lmagery Workshop: International Conference on Pattern Recognition. Stockholm: IEEE Press, p. 9-168 p.
Can we date an artist's work from catalogue photographs?Brown, A., Roderick, G. L., Dee, H. M. & Hughes, L. 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).IEEE Press, p. 558-5636 p.
An Evaluation of Image-Based Robot Orientation EstimationCao, J., Labrosse, F. & Dee, H. M. 2013 Towards Autonomous Robotic Systems.Springer Nature, p. 135-14723 p. (Lecture Notes in Artificial Intelligence)
Reasoning about shadows in a mobile robot environmentFenelon, V., Santos, P. E., Dee, H. M. & Cozman, F. G. 2013 In : Applied Intelligence.38, 4, p. 553-565
Local Orientation Patterns for 3D Surface Texture Analysis of Normal Maps: Application to Facial Skin Condition ClassificationSeck, A., Dee, H. M. & Tiddeman, B. 2013 Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. Bebis, G., Boyle, R., Parvin, B., Koracin, D., Li, B., Porikli, F., Zordan, V., Klosowski, J., Coquillart, S., Luo, X., Chen, M. & Gotz, D. (eds.). Springer Nature, p. 572-5819 p. (Lecture Notes in Computer Science; vol. 8033)
A Novel Image Similarity Measure for Place Recognition in Visual Robotic NavigationCao, J., Labrosse, F. & Dee, H. M. 2012 Advances in Autonomous Robotics: Lecture Notes in Artificial Intelligence. Herrmann, G., Studley, M., Pearson, M., Conn, A., Melhuish, C., Witkowski, M., Kim, J-H. & Vadakkepat, P. (eds.). Springer Nature, Vol. 7429, p. 414-4152 p. (Lecture Notes in Computer Science; vol. 7429)
Face recognition using the POEM descriptorVu, N-S., Dee, H. M. & Caplier, A. 2012 In : Pattern Recognition.45, 7, p. 2478-2488
Building semantic scene models from unconstrained videoDee, H. M., Cohn, A. G. & Hogg, D. C. 2012 In : Computer Vision and Image Understanding.116, 3, p. 446-45611 p.
Coping with Noise in Ultrasound Images: A reviewZwiggelaar, R., Roscoe, J. F. & Dee, H. 2012
Improving bioenergy crop yield and quality through manipulating senescenceRobson, P., Mos, M., Dee, H., Clifton-Brown, J. C. & Donnison, I. 2011 In : Aspects of Applied Biology.112, p. 323-3286 p.
The Perception and Content of Cast Shadows: An Interdisciplinary ReviewDee, H. & Santos, P. E. 2011 In : Spatial Cognition and Computation.11, 3, p. 226-25328 p.
Inspiring women undergraduatesDee, H. & Boyle, R. 2010
Crowd behaviour analysis using histograms of motion directionDee, H. & Caplier, A. 2010
Knowledge-based adaptive thresholding from shadowsDee, H., Santos, P. E. & Fenelon, V. 2010
Knowledge-based adaptive thresholding from qualitative robot localisation using cast shadowsSantos, P. E., Fenelon, V. & Dee, H. 2010
Navigational strategies in behaviour modellingDee, H. M. & Hogg, D. C. 2009 In : Artificial Intelligence.173, 2, p. 329-34214 p.
Qualitative robot localisation using information from cast shadowsFenelon, V., Dee, H. & Santos, P. E. 2009 p. 220-2256 p.
Scene modelling and classification using learned spatial relationsHogg, D. C., Cohn, A. G. & Dee, H. 2009 p. 295-31117 p.
Why are we still here?: Experiences of successful women in computing.Pau, R., Dee, H., Boyle, R. & Petrie, K. E. 2009 p. 233-2375 p.
How close are we to solving the problem of automated visual surveillance? A review of real-world surveillance, scientific progress and evaluative mechanismsDee, H. M. & Velastin, S. A. 2008 In : Machine Vision and Applications.19, 5-6, p. 329-34315 p.
Notes on a qualitative theory of shadowsSantos, P. E., Fenelon, V. & Dee, H. 2008 p. 47-548 p.
Modelling scenes using the activity within themDee, H., Fraile, R., Hogg, D. C. & Cohn, A. G. 2008 p. 394-40815 p.
Using Computer based Systems to Address the Gender Imbalance in ComputingKomisarczuk, J., Petrie, K. E., Ross, M., Paterson, F., Dee, H., Boldyreff, C. & Massey, E. 2008
Navigational strategies and surveillanceHogg, D. C. & Dee, H. 2006 p. 73-819 p.
Supporting undergraduate teaching in histopathology with virtual microscopyQuirke, P., Griffin, N. R., Dee, H., Dixon, M. F. & Waterhouse, M. 2006
On the feasibility of using a cognitive model to filter surveillance dataDee, H. & Hogg, D. C. 2005 p. 34-396 p.
An e-learning system using virtual slidesGriffin, N. R., Quirke, P., Dixon, M. F., Treanor, D., Waterhouse, M. & Dee, H. 2005
A searchable online database of virtual slides.Quirke, P., Lewis, F., Casali, G., Treanor, D., Dee, H., Dixon, M. F. & Edward, S. 2005
Is it Interesting?: Comparing human and machine judgements on the PETS datasetHogg, D. C. & Dee, H. 2004 p. 49-557 p.
Detecting inexplicable behaviourHogg, D. C. & Dee, H. 2004 p. 477-48610 p.
The bigger the better? A new look at virtual learningDee, H. & Tebb, C. 2003 p. 462-4665 p.
ACOM ('Computing for all') : an integrated approach to the teaching and learning of information technologyReffell, P. & Dee, H. 1999 p. 195-1951 p.