Document Detail

Subjective quantification of perceptual interactions among some 2D scientific visualization methods.
MedLine Citation:
PMID:  17080844     Owner:  NLM     Status:  PubMed-not-MEDLINE    
We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect effective data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.
Daniel Acevedo; David H Laidlaw
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on visualization and computer graphics     Volume:  12     ISSN:  1077-2626     ISO Abbreviation:  IEEE Trans Vis Comput Graph     Publication Date:    2006 Sep-Oct
Date Detail:
Created Date:  2006-11-03     Completed Date:  2007-01-12     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9891704     Medline TA:  IEEE Trans Vis Comput Graph     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1133-40     Citation Subset:  -    
Department of Computer Science, Brown University, USA.
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