The estimated rate that information enters the human eye is a staggering 72GB/s. Using very sluggish neurons and a power budget of a few watts the human brain processes this information, creates a percept of a stable world and detects important events occurring within the world, significantly outperforming conventional computer vision methods. How does the brain achieve such efficient and fast processing? Predictive coding is a term used by brain scientists to describe an idea that has its roots in the earliest days of psychology, and had a later echo in the field of cybernetics. The theory proposes that in order to drastically reduce the inherent data processing requirements, and thus achieve efficiency, the brain tries to predict incoming sensory information. It does this for two reasons. First, the processing demands for testing a prediction of what something is are considerably lower than for deducing what something is. Second, prediction failures indicate sensory input that merits extra processing resources, where something is changing or incongruent.
We have recently completed an EPSRC funded project to build the first artificial predictive vision system, which we now wish to build upon. The potential applications of this technology are broad (e.g. healthcare, security, ubiquitous computing), and the technology could be used as part of a standalone computer-based system, or to help or augment a human operator.
In order to predict scenes, an understanding of the scene captured in the current and previous frames is required. For this purpose, we propose to utilise off-the-shelf publicly available research code (in-house, standard toolboxes etc.). For example, methods that can analyse basic motion in the scene (optical) and from this high level physics based motion (e.g. moving car) or human activity (e.g. walking pedestrian) are required. The student will evaluate current available methods on our newly acquired data set.
A variety of PhD projects set around this area of research are available:
· Predicting the next frame in 2D video
· Predicting the next frame in 3D video --- using a 3D (RGBD) camera to gather data.
· Comparing and contrasting deep learning versus predictive coding methods.
· Various levels of image analysis: Motion compensation, Motion analysis – physics based motion and human activity.
· Tracking people and objects in the scene
· 3D modelling of the scene
For more information about this project, please email Prof David Marshall [Email Address Removed]
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas. Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
How to apply:
Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below
This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics
In order to be considered candidates must submit the following information:
- Supporting statement
- In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
- Qualification certificates and Transcripts
- Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
- References x 2
- Proof of English language (if applicable)
If you have any questions or need more information, please contact [Email Address Removed]