Although direct volume rendering is a powerful tool for visualizing complex structures within volume data, the size and complexity of the parameter space controlling the rendering process makes generating an informative rendering challenging. In particular, the specification of the transfer function --- the mapping from data values to renderable optical properties --- is frequently a time-consuming and unintuitive task. Ideally, the data being visualized should itself suggest an appropriate transfer function that brings out the features of interest without obscuring them with elements of little importance. We demonstrate that this is possible for a large class of scalar volume data, namely that where the regions of interest are the boundaries between different materials. A transfer function which makes boundaries readily visible can be generated from the relationship between three quantities: the data value and its first and second directional derivatives along the gradient direction. ...

Direct volume-rendering has proven to be an effective and flexible visualization method for 3D scalar fields. Transfer functions are fundamental

We introduce the contour spectrum, a user interface component that improves qualitative user interaction and provides real-time exact quantification in the visualization of isocontours. The contour spectrum is a signature consisting of a variety of scalar data and contour attributes, computed over the range of scalar values w 2!.We explore the use of surface area, volume, and gradientintegral of the contour that are shown to be univariate B-spline functions of the scalar value w for multi-dimensional unstructured triangular grids. These quantitative properties are calculated in real-time and presented to the user as a collection of signature graphs (plots of functions of w) to assist in selecting relevant isovalues w 0 for informative visualization. For timevarying data, these quantitative properties can also be computed over time, and displayed using a 2D interface, giving the user an overview of the time-varying function, and allowing interaction in both isovalue and timestep. The effectiveness of the current system and potential extensions are discussed.

Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.

Information visualizations must allow users to browse information spaces and focus quickly on items of interest. Being able to see some representation of the entire information space provides an initial gestalt overview and gives context to support browsing and search tasks. However, the limited number of pixels on the screen constrain the information bandwidth and make it difficult to completely display large information spaces. The Information Mural is a two-dimensional, reduced representation of an entire information space that fits entirely within a display window or screen. The mural creates a miniature version of the information space using visual attributes such as grayscale shading, intensity, color, and pixel size, along with anti-aliased compression techniques. Information Murals can be used as stand-alone visualizations or in global navigational views. We have built several prototypes to demonstrate the use of Information Murals in visualization applications; subject matter ...

This paper presents a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values we can display simultaneously. Our technique builds multicolored perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in an element are used to vary the appearance of its pexel. Texture and color patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by varying three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and human visual psychophysics have identified these dimensions as important for the formation of perceptual texture patterns. The pexels are colored using a selection technique that controls color distance, linear separation, and color category. Prop...

In this paper we describe a technique for choosing multiple colours for use during data visualization. Our goal is a systematic method for maximizing the total number of colours available for use, while still allowing an observer to rapidly and accurately search a display for any one of the given colours. Previous research suggests that we need to consider three separate effects during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of these effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty-eight observers to find a colour target in displays that contained differently coloured background elements. Results showed our method can be used to select a group of colours that will provide good differentiation between data elements during data visualization. CR Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces- ergonomics, screen design (graphics,

Scalar fields arise in every scientific application. Existing scalar visualization techniques require that the user infer the global scalar structure from what is frequently an insufficient display of information. We present a visualization technique which numerically detects the structure at all scales, removing from the user the responsibility of extracting information implicit in the data, and presenting the structure explicitly for analysis. We further demonstrate how scalar topology detection proves useful for correct visualization and image processing applications such as image co-registration, isocontouring, and mesh compression. Keywords: Scientific Visualization, Scalar Fields, Curves and Surfaces, Vector Topology 1 Introduction Visualizationof scalar fields is common across all scientific disciplines, includinggeographic data such as altitude and temperature, medical applications with CT and MRI values, and pressure and vorticity magnitude in computational fluid dynamics. ...

Abstract—Visualization can provide valuable assistance for data analysis and decision making tasks. However, how people perceive and interact with a visualization tool can strongly influence their understanding of the data as well as the system’s usefulness. Human factors therefore contribute significantly to the visualization process and should play an important role in the design and evaluation of visualization tools. Several research initiatives have begun to explore human factors in visualization, particularly in perception-based design. Nonetheless, visualization work involving human factors is in its infancy, and many potentially promising areas have yet to be explored. Therefore, this paper aims to 1) review known methodology for doing human factors research, with specific emphasis on visualization, 2) review current human factors research in visualization to provide a basis for future investigation, and 3) identify promising areas for future research. Index Terms—Human factors, visualization, perception, cognitive support, methodology. 1

We describe MGV, an integrated visualization and exploration system for massive multi-digraph navigation.