Authors:
Angus Graeme Forbes
;
Christopher Jette
and
Andrew Predoehl
Affiliation:
University of Arizona, United States
Keyword(s):
Intrinsic Motion Textures, Psychophysics, Perception, Metrics for Motion, Dynamic Textures.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Perception and Cognition in Visualization
;
Visual Representation and Interaction
Abstract:
This paper presents an initial exploration of the plausibility of incorporating subtle motions as a useful modality
for encoding (or augmenting the encoding of) data for information visualization tasks. Psychophysics
research indicates that the human visual system is highly responsive to identifying and differentiating even the
subtlest motions intrinsic to an object. We examine aspects of this intrinsic motion, whereby an object stays in
one place while a texture applied to that object changes in subtle but perceptible ways. We hypothesize that the
use of subtle intrinsic motions (as opposed to more obvious extrinsic motion) will avoid the clutter and visual
fatigue that often discourages visualization designers from incorporating motion. Using transformed video
captures of naturalistic motions gathered from the world, we conduct a preliminary user study that attempts
ascertains the minimum amount of motion that is easily perceptible to a viewer. We introduce metrics which
allow us
to categorize these motions in terms of flicker (local amplitude and frequency), flutter (global amplitude
and frequency), and average maximum contrast between a pixel and its immediate neighbors. Using these
metrics (and a few others), we identify plausible ranges of motion that might be appropriate for visualization
tasks, either on their own or in conjunction with other modalities (such as color or shape), without increasing
visual fatigue. Based on an analysis of these initial preliminary results, we propose that the use of what we
term “intrinsic motion textures” may be a promising modality appropriate for a range of visualization tasks.
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