Techniques for augmenting the visualisation of dynamic raster surfaces

Despite their aesthetic appeal and condensed nature, dynamic raster surface representations such as a temporal series of a landform and an attribute series of a socio-economic attribute of an area, are often criticised for the lack of an effective information delivery and interactivity.In this work, we readdress some of the earlier raised reasons for these limitations -information-laden quality of surface datasets, lack of spatial and temporal continuity in the original data, and a limited scope for a real-time interactivity. We demonstrate with examples that the use of four techniques namely the re-expression of the surfaces as a framework of morphometric features, spatial generalisation, morphing, graphic lag and brushing can augment the visualisation of dynamic raster surfaces in temporal and attribute series

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

Multi-level representation of terrain features on a contour map

Contour lines are important for quantitatively displaying relief and identifying morphometric features on a map. Contour trees are often used to represent spatial relationships between contours and assist the user in analysing the terrain. How- ever, automatic analysis from the contour tree is still limited as features identified on a map by sets of contours are not only characterised by local relationships be- tween contours but also by relationships with other features at different levels of representation. In this paper, a new method based on adjacency and inclusion re- lationships between regions defined by sets of contours is presented. The method extracts terrain features and stores them in a feature tree providing a description of the landscape at multiple levels of detail. The method is applied to terrain analysis and generalisation of a contour map by selecting the most relevant features accord- ing to the purpose of the map. Experimental results are presented and discussed

A contour tree based spatio-temporal data model for oceanographic applications

To present the spatio/temporal data from oceanographic modeling in GIS has been a challenging task due to the highly dynamic characteristic and complex pattern of variables, in relation to time and space. This dissertation focuses the research on spatio-temporal GIS data model applied to oceanographic model data, especially to homogeneous iso-surface data. The available spatio-temporal data models are carefully reviewed and characteristics in spatial and temporal issues from oceanographic model data are discussed in detail. As an important tool for data modeling, ontology is introduced to categorize oceanographic model data and further set up fundamental software components in the new data model. The proposed data model is based on the concept of contour tree. By adding temporal information to each node and arc of the contour tree, and using multiple contour trees to represent different time steps in the temporal domain, the changes can be stored and tracked by the data model. In order to reduce the data volume and increase the data quality, the new data model integrates spatial and temporal interpolation methods within it. The spatial interpolation calculates the data that fall between neighboring contours at a single time step. The Inverse Distance Weighting (IDW) is applied as the main algorithm and the Minimum Bounding Rectangle (MBR) is used to enhance the spatial interpolation performance. The temporal interpolation calculates the data that are not recorded, which fall between neighboring contour trees for adjacent time steps. The “linear interpolation” algorithm is preferred to the “nearest neighbor’s value” and “spline” interpolation methods, for its modest accuracy and the simple implementation scheme. In order to evaluate the support functions of the new data model, a case study is presented with the motivation to show how this data model supports complicated spatio-temporal queries in forecasting applications. This dissertation also showcases some work in contour tree simplification. A new simplification algorithm is introduced to reduce the data complexity. This algorithm is based on the branch decomposition method and supports temporal information integrated into contour trees. Three types of criteria parameters are introduced to run different simplification methods for various applications

Radiomics in Medical Imaging with Application to Surgical Innovation

Modern surgery today has greatly improved healthcare due to technological advancements in medical imaging. It has fostered a culture of innovation that has progressed with continuous and incremental changes towards curing patients’ ailments. With evidence-based assessments gaining prominence in modern surgery, Radiomics has become crucial to resolving problems through the integration of the best scientific data with the correct clinical expertise. As a quantitative approach to medical imaging, Radiomics uses mathematical analysis to improve the data made available to clinicians, which greatly influences their decision-making ability. In this thesis, we focus on two applications: pelvic bone segmentation from CT data for designing patient-specific customizable pessaries; and quantitative assessment of breast morphology, for reconstructive breast surgeries. For pelvic bone segmentation, we investigate several encoder-decoder network configurations trained on limited data and use histogram based features from Radiomics to take a data-centric view towards the problem and boost the model performance on completely unseen data through histogram specification. Then we evaluate the performance on two publicly available CT datasets. For assessment of breast morphology, we propose a novel metric for quantifying the overall dissimilarity between two breast mounds, called VIMA, by using shape and size based features from iso-contours. The methodology was experimented on 3D scans of artificial breasts and found to be highly useful in an intra-operative setting for aiding surgeons during aesthetic breast surgeries

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

The uses of interactive computer graphics for solving differential equations

The aim of this thesis is to investigate the use of interactive computer graphics in a system of programs designed to solve ordinary and partial differential equations.The first chapter is an overall introduction to the work carried out and is followed by a chapter containing a general survey of interactive computer graphics, including its development, available hardware and software, properties of interactive graphics systems and a selection of the current areas of application.The third chapter describes the particular hardware and software used with the ICL 4130 at the University of Keele.The types of ordinary and partial differential equations selected for inclusion in the set of interactive graphics programs are described in Chapter 4, followed by a discussion of the methods of solution chosen for each type of problem. The methods themselves are given in greater detail in the appendix. The chapter ends with a review of the more important ancilliary activities performed within the program. These ares curve fitting, the solution of linear algebraic equations, visual representation of 3-dimensional surfaces and the real time input of algebraic expressions.In Chapter 5 the design of an interactive graphical system for solving mathematical problems is considered with particular reference to differential equations. The design principles are implemented in the three main programs, described in Chapter 6, for the solution of ordinary and partial differential equations.In Chapter 7 some of the advantages of interaction and computer graphics when solving differential equations are illustrated by example problems for each of the three programs. The final chapter contains a discussion of the conclusions and recommendations, based on the experience gained in the use of the programs written, on the uses of interactive computer graphics for solving differential equations