Open to: EU student
Visual Cognition in Virtual Environments: An Interdisciplinary Approach
This fully funded PhD studentship provides an opportunity for a UK/EU student to undertake novel, multidisciplinary research that combines human perception in Psychology with 3D visualisation in Computer Science.
The successful candidate will work closely with a group of experts in these fields. The objective of this PhD project is to produce cutting-edge research on human information processing in virtual environments. In most research on visual cognition, the stimuli are presented on a 2D display. This creates a gap between laboratory research and the real-world because in the real-world these perceptual tasks are usually performed in 3D.
Furthermore, observers often interact with the targets through their own bodily motions. For example, natural head movement will normally define the observer’s view-point, rather than using a mouse or keyboard. Thus a number of important questions cannot be answered by traditional methods. To overcome these limitations, the student will conduct experiments in the state-of-the-art Hull Immersive Visualization Environment (HIVE), which provides large scale 3D displays and high performance motion tracking systems.
This environment allows stimuli to be presented with a high degree of realism, and with more natural interaction through motion tracking. Research projects are likely to focus to one or more of the following topics, depending on the applicant’s background and interests:
* Face/object recognition in virtual environments
* Fast extraction of statistical information (e.g., age group, mood) from a crowd/scene
* Effects of an observer’s self motion and interaction with visual/haptic feedback
* Target cueing/detection in virtual environments
* Effects of fixation plane on visual search
* Multiple object/face tracking
* Localisation of objects in 3D space
– Supervisors: Chang Hong Liu (Psychology) | James Ward (Computer Science) | Tjeerd Jellema (Psychology)
Chang Hong Liu
T. 01482 465572