PhD Proposals
Supervisor: David Pycock
Most of the projects listed below are also described on my WWW pages.
Vehicle Driving Aid: Visual Obstacle Detection
The requirements is to be able to detect and distinguish three broad classes of people from other obstacles so that pedestrian protection devices can be deployed in a collision, prior to impact. The technique employed needs to accommodate very wide variations in light level, adverse weather conditions and recognize small people, large people and large people with a pushchair. The proposed approach is to use stereoscopic colour vision and a model-based interpretation strategy in which components of objects in an image are identified to form an overall interpretation. This will build on previous and current research under my supervision, on model-based interpretation and colour scene interpretation.
Reasoning by Analogy for Image Interpretation
For a number of years there has been considerable research into model-based schemes for interpreting complex images that has resulted in a significant improvement in the ability to analyse selected types of image. However the effort needed to develop these techniques for a particular class of application remains a major obstacle to the widespread adoption of image processing. In recent EPSRC funded research under my supervision robust and adaptable strategies for interpreting complex scenes have been demonstrated that achieve a level of performance comparable with that of a human observer2.
The conceptual objective is to create an image analysis system that can be trained, using examples, how to select an appropriate algorithm. Techniques to recognise both when and how algorithms can be adapted from one domain to another will be required. This will involve the development of algorithms, for example, to recognise both when the difference between images in one domain and another involve a simple transformation and to determine an appropriate transformation. Other algorithms will be needed to identify which of the many alternative algorithms for each stage of image processing are appropriate. It will involve the development of measures to compare computational procedures and the development of ways to describe the relationship between a hierarchical description of the procedures. This is needed so that appropriate alternative image interpretation strategies can be selected.
The proposed research extends the concept of image processing systems based on hierarchical definitions of procedures. A key issue will be to develop strategies for reasoning about action (i.e. image processing actions). This strategy extends the concepts developed by Owen1 for Reasoning by Analogy. The intention is that the analysis system should be able to reason at both the knowledge and the meta-knowledge levels.
1. Owen S, Analogy for Automated Reasoning, Academic Press, 1990.
2. Zhou, P and Pycock D, Robust Statistical Model-Based Cell Image Interpretation, in Proc. BMVC’95, pp 117-127.
Automation of Colposcopy Interpretation
Colposcopy is the first line of investigation in the early detection of Cervical Cancer. It involves staining the cervix and viewing the resulting appearance to identify atypical patterns of vascularisation, and distribution of endometrial cells.
Normally colposcopy is performed in a hospital clinic. Experiments are in hand to evaluate the use of video images that could be collected by a trained nurse and assessed by a clinician on a separate occasion. Automated procedures for evaluating these images would mean that only the potentially abnormal cases would need to be referred back to a consultant. The automated measurements of suitable criteria requires innovation in the development of image interpretation procedures. The particular challenge will be to produce quantitative descriptors for features on a surface in 3-D to identify significant changes. Surface geometry can greatly alter appearance and the human visual system is very adept at interpreting structures on complex surfaces.
In this case it is not practical to use projected light patterns or stereoscopic images. The proposal is to investigate the use of colour and particular shading to infer shape. Shape from shading is a well-established technique but it has not been investigated using colour images to define shading. Colour should improve the performance of shape from shading by enabling the effects of changes in surface orientation and illumination to be more effectively isolated.
Whilst it is not practical top use a pair of “calibrated” stereoscopic images an alternative line of investigation would be to combine data from images taken at positions with a small, ill-defined relative displacement of the view point.
A third strategy would be to either use thermal images on their own or combined with an optical image. Both these strategies have the potential to make it easier to identify patterns of vascularisation.
This research will be conducted in collaboration with Professor David Luesley at the Birmingham Women’s Hospital.
Continuing from previous research on automated cell image interpretation there is scope for a project to investigate how automation can be used in a diagnostic support system for histopatholgic interpretation. This project will involve the investigation of content addressable archives for cell images and the investigation of user interfaces, including immersive environments.
