Andrew Cunningham

An Augmented Reality X-Ray system based on visual saliency

In the past, several systems have been presented that enable users to view occluded points of interest using Augmented Reality X-ray visualizations. It is challenging to design a visualization that provides correct occlusions between occluder and occluded objects while maximizing legibility. We have previously published an Augmented Reality X-ray visualization that renders edges of the occluder region over the occluded region to facilitate correct occlusions while providing foreground context. While this approach is simple and works in a wide range of situations, it provides only minimal context of the occluder object.

Egocentric space-distorting visualizations for rapid environment exploration in mobile mixed reality

Throughout the last decade, mobile information browsing has become a widely-adopted practice. Most of todays mobile internet devices contain facilities to display maps of the users surroundings with points of interest embedded into the map. Other researchers have already explored complementary, egocentric visualizations of these points of interest using mobile mixed reality. However, it is challenging to display off-screen or occluded points of interest. We have designed and implemented space-distorting visualizations to address these situations. Based on the informal user feedback that we have gathered, we have performed several iterations on our visualizations. We hope that our initial results can inspire other researchers to also investigate space-distorting visualizations for mixed and augmented reality.

Evaluating depth perception of photorealistic mixed reality visualizations for occluded objects in outdoor environments

Enabling users to accurately perceive the correct depth of occluded objects is one of the major challenges in user interfaces for Mixed Reality (MR). In this paper, we present an evaluation of depth perception in handheld outdoor mixed reality environment in far-field distances through two photorealistic visualizations of occluded objects (X-ray and Melt) in the presence and absence of a depth cue.

GraphScape: integrated multivariate network visualization

In this paper, we introduce a new method, GraphScape, to visualize multivariate networks, i.e., graphs with multivariate data associated with their nodes. GraphScape adopts a landscape metaphor with network structure displayed on a 2D plane and the surface height in the third dimension represents node attribute. More than one attribute can be visualized simultaneously by using multiple surfaces. In addition, GraphScape can be easily combined with existing methods to further increase the total number of attributes visualized. One of the major goals of GraphScape is to reveal multivariate graph clustering, which is based on both network structure and node attributes. This is achieved by a new layout algorithm and an innovative way of constructing attribute surface, which also allows visual clustering at different scales through interaction. A simplified attribute surface model is also proposed to reduce computation requirement when visualizing large networks. GraphScape is applied to networks of three different size (20, 100, and 1500) to demonstrate its effectiveness.

ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate Data Visualisation

We introduce ImAxes immersive system for exploring multivariate data using fluid, modeless interaction. The basic interface element is an embodied data axis. The user can manipulate these axes like physical objects in the immersive environment and combine them into sophisticated visualisations. The type of visualisation that appears depends on the proximity and relative orientation of the axes with respect to one another, which we describe with a formal grammar. This straight-forward composability leads to a number of emergent visualisations and interactions, which we review, and then demonstrate with a detailed multivariate data analysis use case.

TableMouse: a novel multiuser tabletop pointing device

This paper introduces the TableMouse, a new cursor manipulation interaction technology for tabletop computing, specifically designed to support multiple users operating on large horizontal displays. The TableMouse is a low-cost absolute positioning device utilising visually-tracked infrared light emitting diodes for button state, 3D position, 1D orientation, and unique identification information. The supporting software infrastructure is designed to support up to 16 TableMouse devices simultaneously, each with an individual system cursor. This paper introduces the device and software infrastructure and presents two applications exposing its functionality. A formal benchmarking was performed against the traditional mouse for its performance and accuracy.

Seeing more than the graph: evaluation of multivariate graph visualization methods

Many real-world networks are multivariate, i.e., they have attributes associated with nodes and/or edges. Examples include social networks whose nodes represent people and edges represent relationships. There is usually information about each person (such as name, age, and gender) and the relationship (such type, duration, and strength). Besides common graph analysis tasks (such as identifying the most influential or structurally important nodes), there are more complex analyses for multivariate networks. One of these is the multivariate graph clustering, i.e., identifying clusters formed by nodes that have similar attributes and are close to each other in terms of graph distance. For instance, in social network analysis, it is interesting to sociologists whether or not people with similar characteristics (node attributes) are also connected to each other. Currently there are very few visualization methods available for such analysis.n Graph and multivariate visualization have been well studied separately in the literature. Herman et al. summarized the recent work on graph visualization [3], and Wong and Bergeron covered the development in multivariate visualization [4]. However, there is relatively less work available on multivariate network visualization. Two types of approaches are commonly used. The first one is the mapping approach, which maps attributes to visual elements of a node or edge. A simple example is to map one attribute to node size and another to node color [2]. A more advanced mapping approach uses glyphs to represent node or edge attributes. One such example is to use the length and width of a rectangle node glyph to represent two node attributes [1]. The second one is the 2.5D approach: it uses the third dimension to present the multivariate information, while the graph is shown on a 2D plane. Examples include the recently proposed GraphScape [5], which adopts a landscape metaphor: each attribute is represented by a two-and-a-half- dimensional surface, whose height indicates its value.n Each approach has its strength and weakness. The mapping approach is effective of showing numerical value using visual element such as size, but it can be difficult to compare the value of attributes represented by different elements such as size and color. The problem is alleviated by a carefully designed glyph, but visual complexity increases quickly as the number of attributes that a glyph needs to represent grows. The 2.5D approach is good at showing the distribution of attribute values over the network, but the attribute surface could introduce occlusion and affect the visibility of underlying network.n In this paper, we present a study evaluating the effectiveness of these two approaches for different analysis tasks. We compare the performance of mapping and 2.5D approach in a controlled lab environment. We included both simple tasks (such as identifying nodes with the largest attribute value) and complex tasks (such as multivariate graph clustering). The performance is measured both in terms of accuracy and completion time. The results indicate that statistically mapping approach performs better for the simple tasks, while the 2.5D approach is favored in the complex task. The outcomes from this study provide some guidelines for the design of effective multivariate graph visualization for different analysis tasks.