Adapting Image Analysis Measures of Visual Clutter to Multiple Plane Augmented Reality User Interfaces

This study focuses on understanding and quantifying the effects of clutter in multi-plane Augmented Reality User Interfaces (AR UI’s). It determines if image analysis measures of visual clutter can be expanded from single plane AR UI’s to multi-plane AR UI’s as well as examining the effects of multiple UI planes, UI clutter, and target clutter have on user performance. Image analysis measures of clutter were specifically chosen as they can be applied to complex and naturalistic images that may be found in AR UI applications and have shown use in predicting user performance in single plane AR UI’s. In this experiment, twelve participants performed a visual search task of locating a target object in an array of objects where objects were located in planes of different distances. Participants completed this task under two different clutter levels (low and high) against seven different AR UI plane arrays (2m, 4m, 6m, 2m4m, 2m6m, 4m6m, 2m4m6m) where the target was found in every available AR UI plane for each combination, with repetition. Task performance was measured through response time. Results show significant differences in response time between clutter levels, and number of planes. The end goal of this research is to build upon previous studies to develop a clutter score algorithm for AR UI’s that can predict user performance based on image analysis measures.