The emergence of web-based scientific simulation portals has enabled scientists to quickly generate large complex scientific simulation data using high performance computing resources. The increasing complexity of these datasets has brought with it challenges of data exploration and analysis. An effective means of exploring scientific data is through direct volume rendering. Multi-dimensional transfer functions for direct volume rendering have been shown to be an effective means of extracting features and highlighting through the assignment of color and opacity. However, the complexity of setting parameters can impede the users. ability to answer relevant scientific questions about their data. In this work, we improve the discovery, decision making, and data exploration process by providing additional information based on spatial statistics and feature sculpting methods for massive, multivariate datasets.
As concern for obesity grows, the need for automated and accurate methods to monitor nutrient intake becomes essential as dietary intake provides a valuable basis for managing dietary imbalance. Moreover, as mobile devices with built-in cameras have become ubiquitous, one potential means of monitoring dietary intake is photographing meals using mobile devices and having an automatic estimate of the nutrient contents returned. One of the challenging problems of the image-based dietary assessment is the accurate estimation of food portion size from a photograph taken with a mobile digital camera. In this work, we describe a method to automatically calculate portion size of a variety of foods through volume estimation using an image. These “portion volumes” utilize camera parameter estimation and model reconstruction to determine the volume of food items, from which nutritional content is then extrapolated. In this paper, we describe our initial results of accuracy evaluation using real and simulated meal images and demonstrate the potential of our approach.
When performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page-to-page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.
Large scale projects, such as TeraGrid, launched scientific web portals to allows scientists to use high performance computing resources for simulating more complex phenomina and visualizing their results. However, the complexity of setting volume rendering parameters can impede users in answering relevant scientific questions about their data. Moreover, traditional techniques require users to manipulate complex transfer functions that are difficult for volume rendering novices to understand. In this work, we developed exploration methods in feature-driven or non-parametric way.