An approach to the perceptual optimization of complex visualizations

This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.     

Analyzing the Mallat Wavelet Transform to Delineate Contour and Textural Features

Analyzing natural scenes is made difficult when both contour and textural features are present. The problem of building suitable contour models from such images is compounded given texture region segmentation results in poor edge localization and multiscale edge representations cannot always separate salient contour features from irrelevant textural clutter. To overcome these problems, a novel algorithm is presented which first creates a multiscale edge representation using the Mallat wavelet transform and then recombines the edge map at each scale to create a single contour map where textural clutter has been minimized. This algorithm is then applied to natural and synthetic images containing contour features at different spatial scales and texture of varying spatial frequency and orientation. The results show that contour and textural features can be discriminated at each scale and the resulting contour map serves as a more effective representation on which subsequent localization and recognition tasks are based.     

Computers,postmodernism and the culture of the artificial

The term the artificial can only be given a precise meaning in the context of the evolution of computational technology and this in turn can only be fully understood within a cultural setting that includes an epistemological perspective. The argument is illustrated in two case studies from the history of computational machinery: the first calculating machines and the first programmable computers. In the early years of electronic computers, the dominant form of computing was data processing which was a reflection of the dominant philosophy of logical positivism. By contrast, artificial intelligence (AI) adopted an anti-positivist position which left it marginalised until the 1980s when two camps emerged: technical AI which reverted to positivism, and strong AI which reified intelligence. Strong AI's commitment to the computer as a symbol processing machine and its use of models links it to late-modernism. The more directly experiential Virtual Reality (VR) more closely reflects the contemporary cultural climate of postmodernism. It is VR, rather than AI, that is more likely to form the basis of a culture of the artificial.     

SAR imaging using multidimensional continuous wavelet transform and applications to polarimetry and interferometry

Usual SAR imaging process makes the assumption that the reflectors are isotropic and white (i.e., they behave in the same way regardless the angle from which they are viewed and the emitted frequency within the bandwidth). The multidimensional continuous wavelet transform (CWT) in radar imaging was initially developed to highlight the image degradations due to these assumptions. In this article the wavelet transform method is widened to polarimetry and interferometry fields. The wavelet tool is first used for polarimetric image enhancement, then for coherence optimization in interferometry. This optimization by wavelets, compared with the polarimetric one, gives better results on the coherence level. Finally, a combination of both methods is proposed. © 2005 Wiley Periodicals Inc. Int J Imaging Syst Technol, 14, 206–212, 2004; Published online in Wiley Inter‐Science (www.interscience.wiley.com). DOI 10.1002/ima.20025     

A randomized algorithm for the joining protocol in dynamic distributed networks

We describe a randomized algorithm for assigning neighbours to vertices joining a dynamic distributed network. The aim of the algorithm is to maintain connectivity, low diameter and constant vertex degree. On joining each vertex donates a constant number of tokens to the network. These tokens contain the address of the donor vertex. The tokens make independent random walks in the network. A token can be used by any vertex it is visiting to establish a connection to the donor vertex. This allows joining vertices to be allocated a random set of neighbours although the overall vertex membership of the network is unknown. The network we obtain in this way is robust under adversarial deletion of vertices and edges and actively reconnects itself.     

Code Conjurer: Pulling Reusable Software out of Thin Air

Accelerating the software development process by assembling new applications from existing software assets has been a goal of the IT industry for many years. However, most of today's systematic software reuse uses heavyweight approaches such as product-line engineering. Now, with the explosion in open source software repositories and the advent of a new generation of powerful software search engines, this is set to change. Code Conjurer is an Eclipse plug-in that extracts interface and test information from a developer's coding activities and uses this information to issue test-driven searches to a code-search engine. It presents components matching the developer's needs as reuse recommendations without disturbing the development work. Automated dependency resolution then allows selected components to be woven into the current project with minimal manual effort.     

Radar detection of wetland ecosystems: a review

Periodically, reviews of our knowledge of radar–wetland relationships and detection parameters have been provided by various authors. Since the publication of these works, additional research has been completed. Five major remote sensing journals spanning the years 1965–2007 formed the basis of this review. The vast majority of significant material found its way into these mainstream journals in one aspect or another. A short history of Synthetic Aperture Radar (SAR)–wetlands discovery based on earlier reviews is followed by an update on radar‐related wetland research. Although some trends emerged with regard to which wavelengths or polarizations to use, there was variation in optimum season/time of year and selection of multitemporal imagery. What is evident throughout the recent literature is that multidimensional radar data sets are attaining an accepted role in operational situations needing information on wetland presence, extent and conditions.     

Exploring the benefits of augmented reality documentation for maintenance and repair

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.     

Building a multi-modal Arabic corpus (MMAC)

Traditionally, a corpus is a large structured set of text, electronically stored and processed. Corpora have become very important in the study of languages. They have opened new areas of linguistic research, which were unknown until recently. Corpora are also key to the development of optical character recognition (OCR) applications. Access to a corpus of both language and images is essential during OCR development, particularly while training and testing a recognition application. Excellent corpora have been developed for Latin-based languages, but few relate to the Arabic language. This limits the penetration of both corpus linguistics and OCR in Arabic-speaking countries. This paper describes the construction and provides a comprehensive study and analysis of a multi-modal Arabic corpus (MMAC) that is suitable for use in both OCR development and linguistics. MMAC currently contains six million Arabic words and, unlike previous corpora, also includes connected segments or pieces of Arabic words (PAWs) as well as naked pieces of Arabic words (NPAWs) and naked words (NWords); PAWs and Words without diacritical marks. Multi-modal data is generated from both text, gathered from a wide variety of sources, and images of existing documents. Text-based data is complemented by a set of artificially generated images showing each of the Words, NWords, PAWs and NPAWs involved. Applications are provided to generate a natural-looking degradation to the generated images. A ground truth annotation is offered for each such image, while natural images showing small paragraphs and full pages are augmented with representations of the text they depict. A statistical analysis and verification of the dataset has been carried out and is presented. MMAC was also tested using commercial OCR software and is publicly and freely available.