The VERA information environment

We present three components of a virtual research environment developed for the ongoing Roman excavation at Silchester. These components — Recycle Bridge, XDB cross-database search, and Arch3D — provide additional services around the existing core of the system, run on the Integrated Archaeological Database (IADB). They provide, respectively, embedding of legacy applications into portals, cross-database searching, and 3D visualisation of stratigraphic information.

Virtual reality as a visualisation tool: benefits and constraints

The benefits and applications of virtual reality (VR) in the construction industry have been investigated for almost a decade. However, the practical implementation of VR in the construction industry has yet to reach maturity owing to technical constraints. The need for effective information management presents challenges: both transfer of building data to, and organisation of building information within, the virtual environment require consideration. This paper reviews the applications and benefits of VR in the built environment field and reports on a collaboration between Loughborough University and South Bank University to overcome constraints on the use of the overall VR model for whole lifecycle visualisation. The work at each research centre is concerned with an aspect of information management within VR applications for the built environment, and both data transfer and internal data organisation have been investigated. In this paper, similarities and differences between computer-aided design (CAD) and VR packages are first discussed. Three different approaches to the creation of VR models during the design stage are identified and described, with a view to providing sharing understanding across the interdiscipliary groups involved. The suitable organisation of building information within the virtual environment is then further investigated. This work focused on the visualisation of the degradation of a building, through its lifespan, with the view to provide a visual aid for developing an effective and economic project maintenance programme. Finally consideration is given to the potential of emerging standards to facilitate an integrated use of VR. The convergence towards similar data structures in VR and other construction packages may enable visualisation to be better utilised in the overall lifecycle model.

Visualisation and reconstruction in dentistry

Visualisation in the field of dentistry has nor, thus far, reached the same development as other medical fields. Potential applications of visualisation techniques in this area, however, are many, ranging from educational displays to training for delicate procedures. This paper reports on the investigation of techniques for handling three-dimensional models of teeth, aiming at investigation of dental structures. An algorithm was implemented for this purpose, which reconstructs three-dimensional teeth models from two-dimensional contour slices. Results employing various data sets are presented, including the output of VRML models for exploration.

Reconstruction and 3D visualisation based on objective real 3D based documentation

Reconstructions based directly upon forensic evidence alone are called primary information. Historically this consists of documentation of findings by verbal protocols, photographs and other visual means. Currently modern imaging techniques such as 3D surface scanning and radiological methods (Computer Tomography, Magnetic Resonance Imaging) are also applied. Secondary interpretation is based on facts and the examiner's experience. Usually such reconstructive expertises are given in written form, and are often enhanced by sketches. However, narrative interpretations can, especially in complex courses of action, be difficult to present and can be misunderstood. In this report we demonstrate the use of graphic reconstruction of secondary interpretation with supporting pictorial evidence, applying digital visualisation (using 'Poser') or scientific animation (using '3D Studio Max', 'Maya') and present methods of clearly distinguishing between factual documentation and examiners' interpretation based on three cases. The first case involved a pedestrian who was initially struck by a car on a motorway and was then run over by a second car. The second case involved a suicidal gunshot to the head with a rifle, in which the trigger was pushed with a rod. The third case dealt with a collision between two motorcycles. Pictorial reconstruction of the secondary interpretation of these cases has several advantages. The images enable an immediate overview, give rise to enhanced clarity, and compel the examiner to look at all details if he or she is to create a complete image.

Registration And Visualisation Of Deformation Maps From Terrestrial Radar Interferometry Using Photogrammetry And Structure From Motion

This paper describes a general workflow for the registration of terrestrial radar interferometric data with 3D point clouds derived from terrestrial photogrammetry and structure from motion. After the determination of intrinsic and extrinsic orientation parameters, data obtained by terrestrial radar interferometry were projected on point clouds and then on the initial photographs. Visualisation of slope deformation measurements on photographs provides an easily understandable and distributable information product, especially of inaccessible target areas such as steep rock walls or in rockfall run-out zones. The suitability and error propagation of the referencing steps and final visualisation of four approaches are compared: (a) the classic approach using a metric camera and stereo-image photogrammetry; (b) images acquired with a metric camera, automatically processed using structure from motion; (c) images acquired with a digital compact camera, processed with structure from motion; and (d) a markerless approach, using images acquired with a digital compact camera using structure from motion without artificial ground control points. The usability of the completely markerless approach for the visualisation of high-resolution radar interferometry assists the production of visualisation products for interpretation.

Metadata Visualization of Cultural Heritage Information within a Collaborative Environment

Cultural content on the Web is available in various domains (cultural objects, datasets, geospatial data, moving images, scholarly texts and visual resources), concerns various topics, is written in different languages, targeted to both laymen and experts, and provided by different communities (libraries, archives museums and information industry) and individuals (Figure 1). The integration of information technologies and cultural heritage content on the Web is expected to have an impact on everyday life from the point of view of institutions, communities and individuals. In particular, collaborative environment scan recreate 3D navigable worlds that can offer new insights into our cultural heritage (Chan 2007). However, the main barrier is to find and relate cultural heritage information by end-users of cultural contents, as well as by organisations and communities managing and producing them. In this paper, we explore several visualisation techniques for supporting cultural interfaces, where the role of metadata is essential for supporting the search and communication among end-users (Figure 2). A conceptual framework was developed to integrate the data, purpose, technology, impact, and form components of a collaborative environment, Our preliminary results show that collaborative environments can help with cultural heritage information sharing and communication tasks because of the way in which they provide a visual context to end-users. They can be regarded as distributed virtual reality systems that offer graphically realised, potentially infinite, digital information landscapes. Moreover, collaborative environments also provide a new way of interaction between an end-user and a cultural heritage data set. Finally, the visualisation of metadata of a dataset plays an important role in helping end-users in their search for heritage contents on the Web.

Tools for constraint visualisation: The VIFID/TRIFID tool

Visualisation of program executions has been used in applications which include education and debugging. However, traditional visualisation techniques often fall short of expectations or are altogether inadequate for new programming paradigms, such as Constraint Logic Programming (CLP), whose declarative and operational semantics differ in some crucial ways from those of other paradigms. In particular, traditional ideas regarding the behaviour of data often cannot be lifted in a straightforward way to (C)LP from other families of programming languages. In this chapter we discuss techniques for visualising data evolution in CLP. We briefly review some previously proposed visualisation paradigms, and also propose a number of (to our knowledge) novel ones. The graphical representations have been chosen based on the perceived needs of a programmer trying to analyse the behaviour and characteristics of an execution. In particular, we concentrate on the representation of the run-time values of the variables, and the constraints among them. Given our interest in visualising large executions, we also pay attention to abstraction techniques, i.e., techniques which are intended to help in reducing the complexity of the visual information.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Helicopter vibration sensor selection using data visualisation

The main objective of the project is to enhance the already effective health-monitoring system (HUMS) for helicopters by analysing structural vibrations to recognise different flight conditions directly from sensor information. The goal of this paper is to develop a new method to select those sensors and frequency bands that are best for detecting changes in flight conditions. We projected frequency information to a 2-dimensional space in order to visualise flight-condition transitions using the Generative Topographic Mapping (GTM) and a variant which supports simultaneous feature selection. We created an objective measure of the separation between different flight conditions in the visualisation space by calculating the Kullback-Leibler (KL) divergence between Gaussian mixture models (GMMs) fitted to each class: the higher the KL-divergence, the better the interclass separation. To find the optimal combination of sensors, they were considered in pairs, triples and groups of four sensors. The sensor triples provided the best result in terms of KL-divergence. We also found that the use of a variational training algorithm for the GMMs gave more reliable results.

A data structure for improved gp analysis via efficient computation and visualisation of population measures

Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population measures through an information hypertree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal. © Springer-Verlag 2004.

Visualisation of heterogeneous data with the generalised generative topographic mapping

Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.