PhD Project Proposals in
Computer Vision and Digital Systems
David Pycock
School of Electronic
and Electrical Engineering
Tel: +(0)121 414 4330
Fax.: +(0)121 414 4291
Email: D.Pycock@bham.ac.uk
1. 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.
2. Stereoscopic Active Colour Vision
Stereoscopic vision relies on the verging of binocular views. Until now
this has only been done using monochrome images. The use of colour should
make it possible to more clearly and rapidly identify correspondence as
the feature set provided by a colour scene is inherently richer and produces
less ambiguity than do grey-level images. Whilst dark regions in a colour
scene cannot provide any additional information they are at least as rich
as a monochrome image. In addition there is evidence to show that use of
colour should reduce dependence on ambient illumination. It is, in principle
at least, possible to define invariant measures based on colour change,
independent of intensity change. The greatest potential weakness in using
colour for stereoscopic imaging is in the lower spatial resolution of colour
cameras. However it may be possible to overcome this potential weakness
by taking advantage of the improved robustness and density of colour features.
This project will focus on defining invariant colour features or image
mappings starting with an investigation of colour spaces and lead on to
an investigation of the robustness and accuracy of colour-based stereoscopic
imaging.
3. Heterogeneous Architectures
for Integrating Numeric and Symbolic Processing
This project continues research already under way. To date effort has focused
on an investigation of performance estimation models and developing a programmers
model for a heterogeneous system based on the Contract-Net protocol. The
proposed architecture differs significantly from the many “Network of Workstation
Systems” that are currently under development in the use of an heterogeneous
set of processing modules. The style of task allocation in which the distribution
of tasks to processors depends on the outcome of earlier computations is
an important, novel feature of this strategy. Most computational procedures
that involve a combination of numeric and symbolic data analysis are non-deterministic
in nature and this approach is designed to manage such computation which
is not well handled in traditional schemes of data analysis. Our experience
in attempting to model this system has confirmed our suspicion that existing
modelling strategies are not well suited to analysing the behaviour of
this system. Software simulations have, however, shown that this strategy
has considerable potential to accelerate the speed of computation. We have
demonstrated better than linear speed up in particular contexts. These
experiments have been performed using a homogeneous set of processing modules.
Some of the processing modules were handicapped to simulate heterogeneity.
We are now seeking to develop the hardware elements for this computational
architecture and build a prototype system. The major issue that will need
to be addressed in this next phase is the development of a message interpretation
module. This will be a specialised processor for performing pattern matching
but also able to evaluate the complexity of algorithms. The present programmers
model implements the major facets of the Contract Net protocol in Glenda,
the GNU version of Linda. This programmers model uses the shared memory
management provided by Glenda to implement task announcement, bidding,
the negotiation of “contracts”, the transfer of data and the transfer of
computational procedures. A number of issues in this task allocation or
scheduling process still need to be addressed. These include acquiring
and using knowledge of the capabilities of processing modules, the control
of forward planning; when to stop task planning and to start execution.
4. Model-Based Multi-Modal Image Registration
This research is concerned with automating the registration of multi-modal
medical images. Present registration techniques assume that the corresponding
features in multi-modal images have a similar appearance. This is a manifestly
unsound assumption. Studies have shown that significant errors of registration
occur when such assumptions are made in non-rigid schemes of registration.
The development of new functional imaging modalities means that it is rapidly
becoming more important to be able to register anatomic and functional
images. The approach that we propose is to use automatically generated
models to define the correct registration of images from differing modalities.
The key stages in this process are to identify equivalent topological structures
and landmarks in each modality. The basis of our strategy is the tenet
that topology will define equivalent features in each modality more reliably
than procedures based on the direct detection of grey-level ridges. When
such topological landmarks have been identified we will be able to compute
measures to describe key structures in the image around each landmark that
will be used to define a model of correspondence. As a first step towards
the extraction of topological structures we have investigated algorithms
for computing multi-scale medial axes or cores of an image. This research
has led to an improved algorithm which eliminates many of the anomalies
generated by previous methods and produces an axis that is localised with
greater precision and improved noise immunity. There are four key steps
in the process described above that need further study. These are: 1. Building
a topological description. 2. The interpretation of what is likely to be
a complex topology. 3. Building models that describe corresponding landmarks.
4. Using these concepts for multi-modal medical image registration. It
is anticipated that a research student would concentrate on at the most
two of these topics.
5. 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.
6. Model-Based Interpretation of Stereoscopic
Images
Recent research in the School has led to the development of a technique
for identifying the 3-D structure of textured surfaces using stereoscopic
images and projected light patterns. A hierarchical active contour method
is used to identify the path of the projected stripe in each image and
a 3-D reconstruction formed. The further research proposed will extend
this work by using an active contour to identify the disparity between
the stereoscopic image pair.
Grace AE, Pycock D, Multi-resolution active contour models in textured
stereo images, in Proc. BMVC’96, pp 575-584.