In this paper the results of studies aimed to determinate communication peculiarity between researcher and undefined data visual model are presented. The quantitative features system of visual models and the mode of its measuring are proposed. Derived results allow to forecast cognitive importance for worked up solution visualization tasks. In this item authors describe experimental research technique representing the sequence of three types measuring data model characteristics. It is confirmed high dependence of some interpretability characteristic on researchers personal features. There are assigned visual models attributes allowing to compare representing data modes and to optimize its analysis tools. Conducted researches show appropriateness of transition to use of visualized data successive analysis, based on human perception features and his new data interpretation.
|Состояние||Опубликовано - 1 янв 2016|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition