5410377
Measuring Geodesic Distances on the Space of Bounded Diffeomorphisms
This paper considers the problem of measuring the differences between deformations. It is intended to be applied in the context of applications such as the analysis of sets of non-rigidly registered medical images, where diffeomorphic warps between pairs of images establish a dense pixel-to-pixel correspondence. We introduce a new spline with explicit boundary conditions, and show how this can be used to generate general flows of bounded diffeomorphisms. Techniques are developed to describe an arbitrary continuous diffeomorphism and to invert and interpolate between such diffeomorphisms. We show how these constructions and the geodesic distance can be used in the analysis of training sets of general diffeomorphic warps.
9944487
Constrained Active Appearance Models
Active Appearance Models (AAMs) have been shown to be useful for interpreting images of deformable objects. Here we place the AAM matching algorithm in a statistical framework, allowing extra constraints to be applied. This enables the models to be combined with other methods of object location. We demonstrate how user interaction can be used to guide the search and give results of experiments showing the effect of constraints on the performance of model matching.
18409496
Building optimal 2D statistical shape models
Statistical shape models are used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need, during training, to establish a dense correspondence across a training set of segmented shapes. We show that model construction can be treated as an optimisation problem, automating the process and guaranteeing the effectiveness of the resulting models. This is achieved by optimising an objective function with respect to the correspondence. We use an information theoretic objective function that directly promotes desirable features of the model. This is coupled with an effective method of manipulating correspondence, based on reparameterising each training shape, to build optimal statistical shape models. The method is evaluated on several training sets of shapes, showing that it constructs better models than alternative approaches. q 2003 Elsevier B.V. All rights reserved.
35191926
Automatic Extraction of the Face Identity-Subspace
Facial variation divides into a number of functional subspaces, and ensemblespecific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectationmaximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.
17273589
Developing a Measure of Similarity between Pixel Signatures
Our previous work in computer-aided mammography has used scaleorientation pixel signatures that provide a rich description of local structure. We describe work using the transportation (`earth mover') algorithm to de ne a measure of similarity between two signatures that recognises similar structures whilst remaining robust to background variability and the presence of other structures. Speci cally, we investigate an un-normalised, normalised and novel best-partialmatching (BPM) approach to measuring this transportation distance and compare it with Euclidean distance. Receiver operating characteristic (ROC) methodology is used to compare performance and an example of clinical application is given. The BPM approach outperforms the other methods.
117576
Transforming pixel signatures into an improved metric space
Abstract We address the problem of using scale-orientation pixel signatures to characterise local structure in X-ray mammograms, though the method we report is of general application. When signatures are treated as vectors for statistical analysis, the Euclidean metric is not well behaved. We have previously described a Best Partial Match (BPM) metric that measures signature similarity more naturally, but at high computational cost. We present a method for transforming signatures into a new space in which Euclidean distance approximates BPM distance, allowing BPM distance to be estimated at low computational cost. The new space is constructed using multi-dimensional scaling. The nonlinear transformation between the old and new spaces is learned using support vector regression. We present experimental results for mammographic data.
207655806
Environmental time series analysis and forecasting with the Captain toolbox
The Data-Based Mechanistic (DBM) modelling philosophy emphasises the importance of parametrically efficient, low order, 'dominant mode' models, as well as the development of stochastic methods and the associated statistical analysis required for their identification and estimation. Furthermore, it stresses the importance of explicitly acknowledging the basic uncertainty in the process, which is particularly important for the characterisation and forecasting of environmental and other poorly defined systems. The paper focuses on a Matlab^(R) compatible toolbox that has evolved from this DBM modelling research. Based around a state space and transfer function estimation framework, Captain extends Matlab^(R) to allow, in the most general case, for the identification and estimation of a wide range of unobserved components models. Uniquely, however, Captain focuses on models with both time variable and state dependent parameters and has recently been implemented with the latest methodological developments in this regard. Here, the main innovations are: the automatic optimisation of the hyper-parameters, which define the statistical properties of the time variable parameters; the provision of smoothed as well as filtered parameter estimates; the robust and statistically efficient identification and estimation of both discrete and continuous time transfer function models; and the availability of various special model structures that have wide application potential in the environmental sciences.
14456844
Exploration strategies for mobile robots
The problem of programming a robot to carry out a systematic exploration of its environment using realistic sensors is considered. The robot is modeled as a single point moving in a two-dimensional configuration space populated with visually opaque and transparent obstacles. The robot is equipped with proximity sensors, a vision-based recognition system, and a method of odometry, all of which have some uncertainty associated with their measurements. By using visually distinctive configurations of features in the world as natural landmarks, a series of local maps is constructed. The geometrical relationships between mutually visible landmarks are used to build a relational map from this collection of local maps. This representation forms the basis for a systematic exploration algorithm. The approach is implemented in simulation, and results are presented.<<ETX>>
16474707
Reconstructing Polyhedral Models of Architectural Scenes from Photographs
This paper presents a new image-based modeling method that facilitates the recovery of accurate polyhedral models of architectural scenes. The method is particularly effective because it exploits many of the constraints that are characteristic of architectural scenes. This work is placed in the context of the FaCade project, whose goal is to use images to produce photo-realistic novel views of architectural scenes.
5491850
A comparative study of vision-based lateral control strategies for autonomous highway driving
This paper will present the results of a comparative study of a set of vision-based control strategies that have been applied to the problem of steering an autonomous vehicle along a highway. The aim of this work has been to further our understanding of the characteristics of various control laws that could be applied to this problem with a view to making informed design decisions. The control strategies that we explored include a lead lag control law, a full-state linear controller and input-output linearizing control law. Each of these control strategies was implemented and tested on our experimental vehicle, a Honda Accord LX, both with and without a curvature feedforward component.
13161464
Macroscopic traffic flow modelling and ramp metering control using Matlab/Simulink
Computer programs to simulate traffic flow offer an opportunity to evaluate new strategies for reducing delays, congestion, fuel consumption and pollution. This paper describes a Statistical Traffic Model or STM, which is based on accepted macro-modelling concepts, such as the conservation of vehicles and the fundamental traffic diagram. In this case, the model is constructed using the well known Matlab/Simulink™ software package, so providing an integrated approach for data processing, graphical presentation of data, control system design and macroscopic simulation in one straightforward to use, widely available environment. To illustrate the methodology, the STM is applied to a section of the M3/M27 Ramp Metering Pilot Scheme in the UK. This Highways Agency sponsored project, based in the Southampton area, utilises traffic lights at the on-ramp entrances to regulate access to the main carriageway of the motorway, in an attempt to maintain flow close to the capacity. The paper utilises the model to help design a locally-coordinated ramp metering algorithm, based on proportional-integral-plus (PIP) control methods. In this manner, the STM proves particularly valuable for the application of multi-objective optimisation techniques in the design of new traffic management systems.