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.
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.
9776015
Learning to interpret medical images
Statistical shape and appearance models are proving valuable tools for medical image analysis. In this paper we outline the methodology and highlight important recent developments. In particular, we consider an extension to images of differing modality, and describe an optimisation approach to establishing correspondences across a training set of shapes.
17626745
Design of an Adaptive Architecture for Energy Efficient Wireless Image Communication
With the projected significant growth in mobile internet and multimedia services, there is a strong demand for next-generation wireless appliances capable of image communication. However, wireless image communication faces significant bottlenecks including high energy and bandwidth consumption. Past studies have shown that the bottlenecks to wireless image communication can be overcome by developing adaptive image compression algorithms and dynamically adapting them to current channel conditions and service requirements [1,2].In this paper, we present the design of an adaptive hardware/software architecture that enables adaptive wireless image communication. Through intelligent co-design of the proposed architecture and algorithms, we achieve an architecture which enables not only power and performance efficient implementation, but also fast and efficient run-time adaptation of image compression parameters. To achieve efficient image compression and run-time adaptation, we characterized the adaptation needs of an adaptive image compression algorithm in terms of parameters, and implemented an adaptive hardware/software architecture capable of executing JPEC image compression with different parameters. We present experimental results demonstrating that the proposed architecture enables low overhead adaptation to current wireless conditions and requirements while implementing a low cost (energy and performance) implementation of adaptive image compression algorithms.
110260194
A Comparative Study of Vision-Based Lateral Control Strategies for Autonomous Highway Driving
With the increasing speeds of modern microprocessors, it has become ever more common for computer-vision algorithms to find application in real-time control tasks. In this paper, we present an analysis of the problem of steering an autonomous vehicle along a highway based on the images obtained from a CCD camera mounted in the vehicle. We explore the effects of changing various important system parameters like the vehicle velocity, the look-ahead range of the vision sensor, and the processing delay associated with the perception and control systems. We also present the results of a series of experiments that were designed to provide a systematic comparison of a number of control strategies. The control strategies that were explored include a lead-lag control law, a full-state linear controller, and an input-output linearizing control law. Each of these control strategies was implemented and tested at highway speeds on our experimental vehicle platform, a Honda Accord LX sedan.
2609415
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach’s ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs. CR Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding Modeling and recovery of physical attributes; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture I.4.8 [Image Processing]: Scene Analysis Stereo; J.6 [Computer-Aided Engineering]: Computer-aided design (CAD).
15389117
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
A framework for automatic landmark identification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models, known as point distribution models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation.