31 research outputs found
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
Machine learning methods for discriminating natural targets in seabed imagery
The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems.
These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of â 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation.
Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture
classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world
sonar mosaic imagery.
A number of technical challenges arose and these were
surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation
of pockmark and Sabellaria discrimination is feasible within this framework
The University Defence Research Collaboration In Signal Processing: 2013-2018
Signal processing is an enabling technology crucial to all areas
of defence and security. It is called for whenever humans and
autonomous systems are required to interpret data (i.e. the signal)
output from sensors. This leads to the production of the
intelligence on which military outcomes depend. Signal processing
should be timely, accurate and suited to the decisions
to be made. When performed well it is critical, battle-winning
and probably the most important weapon which youâve never
heard of.
With the plethora of sensors and data sources that are
emerging in the future network-enabled battlespace, sensing
is becoming ubiquitous. This makes signal processing more
complicated but also brings great opportunities.
The second phase of the University Defence Research Collaboration
in Signal Processing was set up to meet these complex
problems head-on while taking advantage of the opportunities.
Its unique structure combines two multi-disciplinary
academic consortia, in which many researchers can approach
different aspects of a problem, with baked-in industrial collaboration
enabling early commercial exploitation.
This phase of the UDRC will have been running for 5 years
by the time it completes in March 2018, with remarkable results.
This book aims to present those accomplishments and
advances in a style accessible to stakeholders, collaborators and
exploiters
Advanced Computational Methods for Oncological Image Analysis
[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with cliniciansâ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operationsâsuch as segmentation, co-registration, classification, and dimensionality reductionâand multi-omics data integration.
All-weather object recognition using radar and infrared sensing
Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on
optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging
(LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new
sensing developments based on long wave polarised infrared (IR) imagery and imaging
radar to recognise objects. First, we developed a methodology based on Stokes parameters
using polarised infrared data to recognise vehicles using deep neural networks. Second,
we explored the potential of using only the power spectrum captured by low-THz radar
sensors to perform object recognition in a controlled scenario. This latter work is based
on a data-driven approach together with the development of a data augmentation method
based on attenuation, range and speckle noise. Last, we created a new large-scale dataset
in the âwildâ with many different weather scenarios (sunny, overcast, night, fog, rain and
snow) showing radar robustness to detect vehicles in adverse weather. High resolution
radar and polarised IR imagery, combined with a deep learning approach, are shown as a
potential alternative to current automotive sensing systems based on visible spectrum optical technology as they are more robust in severe weather and adverse light conditions.UK Engineering and Physical Research Council, grant reference EP/N012402/
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition