5.5 Experiment 3
5.5.1 Data
The data sets for the distance and local shape tasks are the same as in Experiments 1 and 2. The global shape task asks participants to indicate the presence or absence of recognizable shapes, so a class of familiar objects was chosen. Six different fruit shapes were chosen; apple, banana, cherry, grape, orange, and pear (see Figure 5.9). The fruit data are not to scale, instead they are all rescaled to be approximately the same size.
apple
banana
cherry
grape
orange
pear
Figure 5.9: This image shows the complete set of data used for the global shape task of Experiment 3. The data were chosen to be easily recognizable.
5.5.2 Conditions: Visualization techniques
Experiments 1 and 2 use red and blue colors, including a red-grey-blue saturation scale. In the process of publishing and presenting the first two experiments, the red-grey-blue color scale was fre- quently questioned. In particular, some reviewers asked whether a hue only red-yellow-green color scale would lead to better performance. To answer these questions, the color scale in the color map- ping technique for Experiment 3 is changed to red-yellow-green. For consistency, red and blue are changed to red and green throughout Experiment 3. Although this change does make a difference for the distance task, it does not appear to make a significant difference for the local shape task.
The visualizations in Experiments 1 and 2 utilize a regular grid for parts of the display. Texturing a height-field with a low-distortion regular grid is simple. Texturing arbitrary surfaces with a low- distortion regular grid is more difficult. In fact, the regular grid texture is commonly used to show the limitations of automatic surface parameterization techniques. Instead of implementing a technique that could texture the fruit surfaces with a low-distortion regular grid, the regular grid texture is re- moved from all visualizations in Experiment 3. In the techniques using principal curvature texture, the grid texture on the interior surface is replaced with a procedural noise texture which serves to help distinguish the interior from the exterior. In the remaining two techniques, which do not display interior and exterior surfaces simultaneously, neither noise nor grid texture was used.
Color mapping
As already mentioned, the color scale used for the color mapping technique is red-yellow-green. Also, the regular grid present in Experiment 1 is removed. Figure 5.10 shows examples of the im- plementation of the color map visualization for this experiment group. Note that now red indicates that the visible surface geometry is below (or inside) the second surface geometry, yellow indicates the intersection, and green indicates that the visible surface geometry is above (or outside) the second surface geometry.
Figure 5.10: Examples of the color mapping technique. The top image uses the fruit data. The visualization is of the apple and the pear. The two surfaces on bottom left are used to produce the visualization on the bottom right.
Principal curvature texture
There are two changes from the version in Experiments 1 and 2. First, the colors are changed. Second, and more significantly, the regular grid texture on the interior has been replaced with proce- dural noise [Ola05]. The noise texture is more easily applied to arbitrary closed surfaces (e.g. fruit shapes) than is the regular grid. Figure 5.11 shows examples of trials used in this set of experiments.
Figure 5.11: Examples of the principal curvature texture technique. The top image uses the fruit data. The visualization is of the apple and the pear. The two surfaces on the bottom left are used to produce the visualization on the bottom right.
Principal curvature texture with cast shadows
Again, the only changes as compared to Experiments 1 and 2 is color and interior texture. See Figure 5.12.
Figure 5.12: Examples of the principal curvature texture with cast shadows technique. The top image uses the fruit data. The visualization is of the apple and the pear. The two surfaces on the bottom left are used to produce the visualization on the bottom right.
Point-correspondence glyphs
This set of user studies uses an additional baseline comparison technique, point-correspondence glyphs. Line glyphs4on surfaces are commonly used to show a scalar field on a surface. They are also frequently used in uncertainty visualization to show the error or uncertainty in the local surface position. When used for uncertainty, they effectively sample the “error” surface and connect a point on the visible surface to its corresponding “error” point. It is this property that makes them attractive
4Recall thatline glyphsare line segments attached at one end to the surface and extending for a distance related to the
Figure 5.13: Examples of the point-correspondence glyphs technique. The top image uses the fruit data. The visualization is of the apple and the pear. The two surfaces on the bottom left are used to produce the visualization on the bottom right.
for layered-surface visualization.
In this technique, instead of the line glyph indicating error it indicates the position of the corre- sponding point on the second surface. Here the visible surface is theinteriorof the refactored inter- secting surfaces with color used to label the original data source. The second surface, the one sampled by the unattached end of the point-correspondence glyphs, is theexteriorof the refactored intersecting surfaces. The sampling of the exterior surface by the point correspondence glyphs is Poisson-like and relatively sparse. The sampling rate is the same as that used for the principal curvature texture tech- niques. See Figure 5.13. The intersecting surfaces are refactored because the correspondence glyphs
would be inside the visible surface in some regions otherwise and would not be of any benefit.
Principal curvature texture with correspondence glyphs
This visualization technique combines principal curvature texture with point-correspondence glyphs. The interior and exterior surfaces are displayed as in the other curvature texture techniques; the in- terior is grey with a noise texture and the exterior is translucent with curvature-glyph texture using color as a data label. Additionally, yellow point-correspondence glyphs connect the center of each curvature-glyph texture to its corresponding interior point. See Figure 5.14.
5.5.3 Tasks
The distance task and local shape tasks are included in this set of experiments. They are presented as they were in Experiment 1 except for the number of visualization techniques and the number of distinct trials. Because there are now 5 visualization techniques for evaluation, the range of differences to compare is smaller than in Experiment 1 (as in Experiment 2, by removing the extreme ends of the ranges) to keep the total number of trials about the same.
The third user study in this set is the global shape task. One reason for the inclusion of some global shape task is that the scientists indicated interest in questions about global trends (i.e. percentage of inter-surface distance below some threshold). These questions were very difficult to design shape perception task around but could be touched on with a global task. Further, because of the complexity of the visualizations, it seemed expedient to include a task that might show that individual global shapes could still be understood.
I decided to make the global shape task fit into the framework of the other tasks (forced choice between two options). I considered many versions of the global shape task of the form, “Does one of the two surfaces depicted in this imagepossess some property?” For instance, one of the rejected versions included expressive faces. Expressive faces (e.g. surprised, sad, happy, etc.) were rejected because of the perceptual machinery in the human visual system specifically tuned to recognizing faces [KMC97]; there was concern that the results would come into question due to the unquantifi-
Figure 5.14: Examples of the principal curvature with point-correspondence glyphs technique. The top image uses the fruit data. The visualization is of the apple and the pear. The two surfaces on the bottom left are used to produce the visualization on the bottom right.
able contribution of this machinery. Other potential sets of data were rejected for not being easily recognized by a naive population or for not clearly requiring perception of the global shape.
The global shape task uses a small set of recognizable fruit as data. In fact, most types of fruit have many common varieties, so participants are given a set of images showing each fruit model rendered alone to avoid confusion due to preferred varieties5(see Figure 5.9). Participants are shown a random pairing of fruit (same-same pairings do not occur) and asked if one of the six types of fruit is present
5For example, one participant questioned the shape of the pear until I explained that it was an European pear not an
Figure 5.15: A color-sensitivity test in the style of Ishihara [Ish94]. An individual with normal color vision will see the number 5. An individual with red/green color blindness will see the number 2.
or not.
5.5.4 Questionnaire
This set of user studies included a questionnaire. The questionnaire consisted of two questions:
1. On a scale of 1 to 9, how well did each display present the information you needed to perform the task?
2. How would you rank the effectiveness of each display?
These are essentially the same questions asked in Experiment 1.