User-centered development of a Virtual Research Environment to support collaborative research events

This paper discusses the user-centred development process within the Collaborative Research Events on the Web (CREW) project, funded under the JISC Virtual Research Environments (VRE) programme. After presenting the project, its aims and the functionality of the CREW VRE, we focus on the user engagement approach, grounded in the method of co-realisation. We describe the different research settings and requirements of our three embedded user groups and the respective activities conducted so far. Finally we elaborate on the main challenges of our user engagement approach and end with the project’s next steps

Contouring with uncertainty

As stated by Johnson [Joh04], the visualization of uncertainty remains one of the major challenges for the visualization community. To achieve this, we need to understand and develop methods that allow us not only to consider uncertainty as an extra variable within the visualization process, but to treat it as an integral part. In this paper, we take contouring, one of the most widely used visualization techniques for two dimensional data, and focus on extending the concept of contouring to uncertainty. We develop special techniques for the visualization of uncertain contours. We illustrate the work through application to a case study in oceanography

An intelligent real time 3D vision system for robotic welding tasks

MARWIN is a top-level robot control system that has been designed for automatic robot welding tasks. It extracts welding parameters and calculates robot trajectories directly from CAD models which are then verified by real-time 3D scanning and registration. MARWIN's 3D computer vision provides a user-centred robot environment in which a task is specified by the user by simply confirming and/or adjusting suggested parameters and welding sequences. The focus of this paper is on describing a mathematical formulation for fast 3D reconstruction using structured light together with the mechanical design and testing of the 3D vision system and show how such technologies can be exploited in robot welding tasks

Uncertain Flow Visualization using LIC

In this paper we look at the Line Integral Convolution method for flow visualization and ways in which this can be applied to the visualization of two dimensional, steady flow fields in the presence of uncertainty. To achieve this, we start by studying the method and reviewing the history of modifications other authors have made to it in order to improve its efficiency or capabilities, and using these as a base for the visualization of uncertain flow fields. Finally, we apply our methodology to a case study from the field of oceanography

Sketch-based 3D Shape Retrieval using Convolutional Neural Networks

Retrieving 3D models from 2D human sketches has received considerable attention in the areas of graphics, image retrieval, and computer vision. Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features. We argue that this two stage approach (view selection -- matching) is pragmatic but also problematic because the "best views" are subjective and ambiguous, which makes the matching inputs obscure. This imprecise nature of matching further makes it challenging to choose features manually. Instead of relying on the elusive concept of "best views" and the hand-crafted features, we propose to define our views using a minimalism approach and learn features for both sketches and views. Specifically, we drastically reduce the number of views to only two predefined directions for the whole dataset. Then, we learn two Siamese Convolutional Neural Networks (CNNs), one for the views and one for the sketches. The loss function is defined on the within-domain as well as the cross-domain similarities. Our experiments on three benchmark datasets demonstrate that our method is significantly better than state of the art approaches, and outperforms them in all conventional metrics.Comment: CVPR 201