Personal profile

Contact Details


Phone:+44 (0)121 204 3313 


Research Interests

My research focuses on human visual perception including changes in vision associated with ageing and expertise. I am also interested in the applications of visual perception computer vision and robotics. 

My main focus is on the perception of visual texture (second-order vision) and how this enables humans to distinguish between changes in illumination from changes in the material properties of surfaces. This is combined with an interest in shape-from-shading, the role of shadows in object perception, and depth perception.

Older adults find it harder to see visual texture than younger adults and this may lead to difficulty in judging the shape of uneven surfaces such as steps. Here I am interested in how step markings might be manipulated to influence toe clearance in older adults.

With regard to expertise, along with my PhD student Emil Skog and collauges at the Ordnance Survey I have studied stereopsis in remote sensing serveyors who use stereoscopic aerial images to make and modify maps. We foud that they make more use of stereoscopic (3D) vision than do untrained individuals.

In Computer Vision the same mechanisms that we think are used by humans to separate illumination and material changes can be used by machines for the same purpose (intrinsic image extraction). Here I am also interested on how human edge and surface processing can be modelled with machine learning and deep neural networks to aid in the reconstruction of 3D shape from 2D images.

I am Director of the Aston Research Centre for Health in Ageing (ARCHA), a member of the, Cognition & Neuroscience Research Group, Aston Laboratory for Immersive Virtual Environments (ALIVE) and the Aston Insitute for Health and Neurodevelopment

Research Projects/Collaborations

ROSSINI: Reconstructing 3D structure from single images: a perceptual reconstruction approach. 2019 - 2023: ESPRC funded joint with Surrey and Southampton.

ROSSINI will develop a new machine vision system for 3D reconstruction that is more flexible and robust than previous methods. Focussing on static images, we will identify key structural features that are important to humans. We will combine neural networks with computer vision methods to form human-like descriptions of scenes and 3D scene models. Our aims are to (i) produce 3D representations that look correct to humans even if they are not strictly geometrically correct (ii) do so for all types of scene and (iii) express the uncertainty inherent in each reconstruction. To this end we will collect data on human interpretation of images and incorporate this information into our network. Our novel training method will learn from humans and existing ground truth datasets; the training algorithm selecting the most useful human tasks (i.e. judge depth within a particular image) to maximise learning. Importantly, the inclusion of human perceptual data should reduce the overall quantity of training data required, while mitigating the risk of over-reliance on a specific dataset. Moreover, when fully trained, our system will produce 3D reconstructions alongside information about the reliability of the depth estimates.


Using Classification Images to Unlock Expertise in Judgements of 3D Aerial Geography. Co-funder Ordnance Survey.

The Ordnance Survey is the UKs leading mapping company providing authoritative mapping data to government, local authorities and business for the UK with an increasingly global reach. A key element of this work is photogrammetric surveying in which mapping data is created from 3D aerial images. While this task is performed well, the features used are not well understood and surveying skills are largely gained though experience. We have developed a novel technique to derive internal templates for 3D geographical features. This technique will be used to improve training and to develop computer vision systems .

Teaching Activity

First year PY1505 Foundations of Contemporary Psychology

Second year PY2501 Research Methods and Data Analysis

MSc Psychology (Conversion) PY4511 Research Methods and Data Analysis


BEng Electrical and Electronic Engineering, Brunel.

Diploma in Psychology (Conversion), Open.

PhD Communication and Neuroscience, Keele.

PhD Supervision

Hannah Broadbent (Joint University of Nottingham)

Emil Skog

Membership of Professional Bodies

Applied Vision Association (Member and past Chair).

Vision Sciences Society

British Machine Vision Association

Visual Image Interpreation In Humans and Machnies (EPSRC Network, Chair)

Professional/editorial offices

Member of EPSRC Peer Review College + occational panel member / chair


Aug 2022 - Present: Professor of Applied Visual Perception

Oct 2018 - July 2022: Reader in Psychology, Aston University.

2016 - 2018: Deputy Director of Education, College of Life and Environmental Sciences, University of Birmingham.

2006 - 2018: Senior Lecturer, Psychology, University of Birmingham.

1999 - 2006: Lecturer, Psychology, University of Birmingham.

1996 - 1999: Research Fellow, University of Birmingham.

1995 - 1996: Higher Scientific Officer, Forensic Science Service.

1993 - 1995: Research Fellow, Electronics, Brunel University.


Director of Aston Research Centre for Health in Ageing (2023 - )

Director of Research Degree Programmes: HLS (2020 - )

Director of Operations, Psychology (2021 - )

Deputy Head of Psycology (2019 - 2021)

Acting Dean of the Aston Graduate School (Feb-July 2020)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


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