Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

Abstract

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.

Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology

“…Reflected in the objects in nature, that is, changes in the boundaries, backgrounds, materials, etc. of the objects lead to changes in brightness in the image [8]. Edge detection is the most basic tool for image segmentation, and it plays a great role in the discovery, recognition and segmentation of an object in an image.…”

Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

“…Pioneering 3D vector and tensor field studies have largely focused on univariate comparisons, such as vector speed between two locations [30], tracing a single tract [46], reading quantities at each sampling site [47], and showing depth and distances between adjacent occluded tracts [48] [49]. An exception is the study by Acevedo and Laidlaw [50] in which participants were to discriminate boundaries through a set of size-varying circles and must visually derive groups from visualization.…”

“…and discrimination (e.g., how much higher?) tasks inspired by Borgo et al [64] and Zhao et al [47], so as to address the goal of design for perceptually accurate visualizations.…”

Bivariate Separable-Dimension Glyphs can Improve Visual Analysis of Holistic Features

“…Imagine visual search in a 3D large-magnitude-range vector field, where the differences between the smallest vector magnitude and the largest magnitude reach 10 12 . Threedimensional bivariate glyph scene of length y − length y (co-centric cylinders) carrying parallel line lengths of the exponent and digit of scientific notation (aka splitVectors) (Figure 1 (e)) achieved up to ten times better accuracy than a single direct depiction of linear magnitude mapping (Figure 1 (f)) for quantitative discrimination tasks requiring participants to read quantities at a single sampling site or visually compute ratios of two vector magnitudes [8]. How-ever, this bivariate splitVectors glyph also increases task completion time for an apparently simple comparison task between two vector magnitudes in 3D.…”

“…Our method utilizes the fact that binding between separable variables is not always successful and a viewer can thus adopt a sequential task-driven viewing strategy based on visual hierarchy theory [12] to obtain gross regional distribution of larger exponents. After this, a lower-order visual com- The length y − length y bivariate glyph design (e) were ten times more accurate than the linear glyphs (f) for quantitative discrimination tasks but was not efficient for comparing two vector magnitudes [8].…”