Publications
157 results found
Minoi J-L, Gillies D, 2011, A Tensor-based Multivariate Statistical Model for 3D Face and Facial Expression Recognition, 2011 7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN ASIA (CITA 11)
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- Citations: 1
Minoi J-L, Gillies D, 2011, Statistical Analysis of Facial Expression on 3D Face Shapes, ASSISTIVE AND AUGMENTIVE COMMUNICATION FOR THE DISABLED: INTELLIGENT TECHNOLOGIES FOR COMMUNICATION, LEARNING AND TEACHING, Pages: 224-247
Mishra A, Gillies D, 2010, Validation Issues in Regulatory Module Discovery, Pages: 369-380
Then SK, Gillies D, Venilla R, et al., 2010, Biomechanical modelling of the upper human airway, Meeting of the Anaesthetic-Research-Society, Publisher: OXFORD UNIV PRESS, Pages: 521-522, ISSN: 0007-0912
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- Citations: 1
Gillies D, 2010, B-splines, Wiley Interdisciplinary Reviews: Computational Statistics, Vol: 2, Pages: 237-242, ISSN: 1939-5108
B-splines are a family of smooth curves that can be constructed to interpolate or approximate a set of control points. They are used extensively for curve and surface design in engineering and media applications. Their popularity comes from the fact that they offer a simple and intuitive means of adjusting the shape of a curve or surface interactively. Any point on a B-spline curve or surface is defined as a local blend of the control points. The most widely used blending functions are cubic. Higher order blending makes the surface smoother and consequently less detailled. The normal formulation of the B-spline blend is in a parametric space where the control points are equally distributed. Non uniform splines use an irregular distribution of the control points to create special effects, such as discontinuities in the curve or surface. Rational splines provide a further means of user interaction by weighting each point such that the curve is pulled more strongly towards the higher weights. © 2010 John Wiley & Sons, Inc.
Thomaz CE, Giraldi GA, da Costa JFP, et al., 2010, A simple and efficient supervised method for spatially weighted PCA in face image analysis, Departmental Technical Report: 10/13, Publisher: Department of Computing, Imperial College London, 10/13
Principal Component Analysis (PCA) is an example of a successful unsupervised statisticaldimensionality reduction method, especially in small sample size problems. Despitethe well-known attractive properties of PCA, the traditional approach does not incorporateprior information extracted from a specific domain knowledge. The developmentof techniques that bring together dimensionality reduction and prior knowledge can beperformed in the framework of supervised learning methods, like Fisher DiscriminantAnalysis. Semi-supervised methods can also be applied if only a small number of labeledsamples is available. In this paper, we propose a simple and efficient supervised methodthat allows PCA to incorporate explicitly domain knowledge and generates an embeddingspace that inherits its optimality properties for dimensionality reduction. The methodrelies on discriminant weights given by separating hyperplanes to generate the spatiallyweighted PCA. Several experiments using 2D frontal face images and different data setshave been carried out to illustrate the usefulness of the method for dimensionality reduction,classification and interpretation of face images.
Gillies DF, Thornley D, Bisdikian C, 2010, Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks, The Computer Journal, Vol: 53, Pages: 493-502
Thornley DJ, Gillies DF, Bisdikian C, 2009, A stochastic process algebraic abstraction of detection evidence fusion in tactical sensor networks, Proceedings of SPIE - The International Society for Optical Engineering, Vol: 7348, ISSN: 0277-786X
The output of a sensor network intended to detect events or objects generally comprises evidentiary reports of features in the environment that may correspond to those phenomena. Signals from multiple sensors are commonly fused to maximize fidelity of detection through for example synergy between different modes of detection, or simple confirmation. We have previously demonstrated the ability to calculate the meaning of a location report as a probability distribution over potential ground truths by using a stochastic process algebraic model compiled to a discrete-state, continuous-time Markov chain, and performing a transient analysis which resembles the process of parameterizing a Bayesian network. We introduce an approach to representing temporal fusion of multiple heterogeneous sensor detections with different modalities and timing characteristics using a stochastic process algebra. This facilitates analysis of probabilistic properties of the system, and inclusion of those properties into larger models. The formal models are translated into continuous time Markov chains, which provide an important trade-off between the approximation of timing information against complexity of analysis. This is vital to the investigation of analytic computation in real world problems. We illustrate this with an example detection-oriented sensing service model emphasizing the impact of timing. Detection probability and confidence is an essential aspect of the quality of information delivered by a sensing service. The present work is part of an effort to develop a formal event detection calculus that captures the essence of sensor information relating to events, such that features and dependencies can be exploited in re-usable, extendible compositional models. © 2009 SPIE.
Tan CHJ, Gillies DF, 2009, Generating reliable Quality of Information (QoI) metrics for target tracking, Proceedings of SPIE - The International Society for Optical Engineering, Vol: 7336, ISSN: 0277-786X
Recently considerable research has been undertaken into estimating the quality of information (QoI) delivered by military sensor networks. QoI essentially estimates the probability that the information available from the network is correct. Knowledge of the QoI would clearly be of great use to decision makers using a network. An important class of sensors, that provide inputs to networks in real-life, are concerned with target tracking. Assessing the tracking performance of these sensors is an essential component in estimating the QoI of the whole network We have investigated three potential QoI metrics for estimating the dynamic target tracking performance of systems based on some state estimation algorithms. We have tested them on different scenarios with varying degrees of tracking difficulty. We performed experiments on simulated data so that we have a ground truth against which to assess the performance of each metric. Our measure of ground truth is the Euclidean distance between the estimated position and the true position. Recently researchers have suggested using the entropy of the covariance matrix as a metric of QoI [1][2]. Two of our metrics were based on this approach, the first being the entropy of the co-variance matrix relative to an ideal distribution, and the second is the information gain at each update of the covariance matrix. The third metric was calculated by smoothing the residual likelihood value at each new measurement point, similar to the model update likelihood function in an IMM filter. Our experiment results show that reliable QoI metrics cannot be formulated by using solely the covariance matrices. In other words it is possible that a covariance matrix can have high information content, while the position estimate is wrong. On the other hand the smoothed residual likelihood does correlate well with tracking performance, and can be measured without knowledge of the true target position. © 2009 SPIE.
Damarla T, Thornley D, Gillies D, et al., 2009, Toward efficient quality of information estimation in simultaneous acoustic tracking and classification of multiple targets, 12th International Conference on Information Fusion, Publisher: IEEE, Pages: 54-61
An individual sensor's information output is often insufficient for an application, with ambiguities that require refinement or corroboration by fusion with information from other sensors. Fusion of multiple information sources is performed to create an information product of higher quality of information (QoI) that supports more effective intelligence, surveillance, and reconnaissance (ISR). In this paper we present an approach to determining the QoI attributes (metadata) relevant to tracking and classification of multiple vehicles, and the necessary weighting (qualifying) terms, as information derived from multiple acoustic sensors is fused. Field trial data is used to validate the conclusions.
Thomaz CE, do Amaral V, Giraldi GA, et al., 2009, A multi-linear discriminant analysis of 2D frontal face images, 2009 XXII BRAZILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING (SIBGRAPI 2009), Pages: 216-+, ISSN: 1530-1834
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- Citations: 3
Mishra A, Gillies D, 2008, Data integration for regulatory gene module discovery, Vol: 2, Pages: 516-529
This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data relating to the functioning of individual genes can be drawn from many different and diverse experimental techniques. Each piece of data provides information on a specific aspect of the cell regulation process. The chapter argues that integration of these diverse types of data is essential in order to identify biologically relevant regulatory modules. A concise review of the different integration techniques is presented, together with a critical discussion of their pros and cons. A very large number of research papers have been published on this topic, and the authors hope that this chapter will present the reader with a high-level view of the area, elucidating the research issues and underlining the importance of data integration in modern bioinformatics. © 2009, IGI Global.
Thornley DJ, Bisdikian C, Gillies DF, 2008, Using stochastic process algebra models to estimate the quality of information in military sensor networks, Conference on Modeling and Simulation for Military Operations III, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Minoi JL, Gillies DF, 2008, Sub-Tensor decomposition for Expression Variant 3D Face Recognition, 3rd International Conference on Geometric Modeling and Imaging GMAI2008, Publisher: IEEE, Pages: 108-113
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation of collections of face models with multiple formation factors across various modes, without destroying the detail of each other. Using multi-linear single value decomposition (SVD) yields better recognition rates than principal component analysis. We have used a set of landmarks as the input data for our multi-linear SVD recognition experiments. Our results have shown that the choice of landmarks may contribute to the accuracy of recognition. We have used the face action coding system (FACS) framework for manual selection of landmarks on prominent facial features as well as on muscle areas.
Mishra A, Gillies D, 2008, Semi supervised spectral clustering for regulatory module discovery, 5th International Workshop on Data Integration in the Life Sciences, Publisher: SPRINGER-VERLAG BERLIN, Pages: 192-203, ISSN: 2366-6323
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- Citations: 1
Minoi J-L, Amin SH, Thomaz CE, et al., 2008, Synthesizing Realistic Expressions in 3D Face Data Sets, 2nd IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2008), Publisher: IEEE, Pages: 253-+
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- Citations: 1
Amin SH, Gillies DF, 2008, Quantitative Assessment of the Accuracy of 3D Face Shape Reconstruction, 2nd IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 2008), Publisher: IEEE, Pages: 314-+
Thomaz CE, Duran FLS, Busatto GF, et al., 2007, Multivariate statistical differences of MRI samples of the human brain, 19th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2006), Publisher: SPRINGER, Pages: 95-106, ISSN: 0924-9907
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- Citations: 21
Rodrigues MAF, Silva WB, Barbosa Neto ME, et al., 2007, An interactive simulation system for training and treatment planning in orthodontics, COMPUTERS & GRAPHICS-UK, Vol: 31, Pages: 688-697, ISSN: 0097-8493
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- Citations: 10
Thomaz CE, Boardman JP, Counsell S, et al., 2007, A multivariate statistical analysis of the developing human brain in preterm infants, IMAGE AND VISION COMPUTING, Vol: 25, Pages: 981-994, ISSN: 0262-8856
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- Citations: 24
Amin SH, Gillies D, 2007, Analysis of 3D face reconstruction, 14th International Conference on Image Analysis and Processing, Publisher: IEEE COMPUTER SOC, Pages: 413-+
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- Citations: 5
Minoi J-L, Gillies D, 2007, 3D Facial Expression Analysis and Deformation, Symposium on Applied Perception in Graphics and Visualization, Publisher: ASSOC COMPUTING MACHINERY, Pages: 138-138
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- Citations: 2
Amin SH, Gillies D, 2007, Quantitative analysis of 3D face reconstruction using annealing based approach, 1st IEEE International Conference on Biometrics - Theory, Applications and Systems (BTAS 07), Publisher: IEEE, Pages: 107-112
Marvasti S, Ghandi M, Marvasti F, et al., 2007, Multiple wavelet denoising for embolic signal enhancement, IEEE International Conference on Telecommunications/IEEE Malaysia International Conference on Communications (ICT-MICC 2007), Publisher: IEEE, Pages: 658-+
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- Citations: 1
Thomaz C, Kitani E, Gillies D, 2006, A Maximum Uncertainty LDA-based approach for Limited Sample Size problems - with application to Face Recognition, Journal of the Brazilian Computer Society, Vol: 12
Thomaz C, Boardman J, Counsell S, et al., 2006, A Whole Brain Morphometric Analysis of Changes Associated with Preterm Birth, SPIE Medical Imaging 2006: Image Processing, Publisher: SPIE, Pages: 1903-1910
Thomaz CE, Kitani EC, Gillies DF, 2006, A maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition, Journal of the Brazilian Computer Society, Vol: 12, Pages: 7-18, ISSN: 0104-6500
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or pre-processed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this study, a new LDA-based method is proposed. It is based on a straightforward stabilisation approach for the within-class scatter matrix. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other LDA-based methods. The classification results indicate that our method improves the LDA classification performance when the within-class scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features. Since statistical discrimination methods are suitable not only for classification but also for characterisation of differences between groups of patterns, further experiments were carried out in order to extend the new LDA-based method to visually analyse the most discriminating hyper-plane separating two populations. The additional results based on frontal face images indicate that the new LDA-based mapping provides an intuitive interpretation of the two-group classification tasks performed, highlighting the group differences captured by the multivariate statistical approach proposed.
Kitani EC, Thomaz CE, Gillies DF, 2006, A statistical discriminant model for face interpretation and reconstruction, SIBGRAPI 2006: XIX BRAZILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING, PROCEEDINGS, Pages: 247-+, ISSN: 1530-1834
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- Citations: 6
Thomaz CE, Aguiar NAO, Oliveira SHA, et al., 2006, Extracting discriminative information from medical images: A multivariate linear approach, 19th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2006), Publisher: IEEE COMPUTER SOC, Pages: 113-+, ISSN: 1530-1834
Dosis A, Bello F, Aggarwal R, et al., 2005, Synchronised video and motion analysis for the assessment of procedures in the operating theatre, Archives of Surgery, Vol: 140, Pages: 293-299
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