Paper
22 May 1995 Identification of the ideal clutter metric to predict time dependence of human visual search
Joan F. Cartier, David H. Hsu
Author Affiliations +
Abstract
The Army Night Vision and Electronic Sensors Directorate (NVESD) has recently performed a human perception experiment in which eye tracker measurements were made on trained military observers searching for targets in infrared images. This data offered an important opportunity to evaluate a new technique for search modeling. Following the approach taken by Jeff Nicoll, this model treats search as a random walk in which the observers are in one of two states until they quit: they are either searching, or they are wandering around looking for a point of interest. When wandering they skip rapidly from point to point. When examining they move more slowly, reflecting the fact that target discrimination requires additional thought processes. In this paper we simulate the random walk, using a clutter metric to assign relative attractiveness to points of interest within the image which are competing for the observer's attention. The NVESD data indicates that a number of standard clutter metrics are good estimators of the apportionment of observer's time between wandering and examining. Conversely, the apportionment of observer time spent wandering and examining could be used to reverse engineer the ideal clutter metric which would most perfectly describe the behavior of the group of observers. It may be possible to use this technique to design the optimal clutter metric to predict performance of visual search.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joan F. Cartier and David H. Hsu "Identification of the ideal clutter metric to predict time dependence of human visual search", Proc. SPIE 2470, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VI, (22 May 1995); https://doi.org/10.1117/12.210069
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Eye

Reverse modeling

Data modeling

Eye models

Visualization

Target detection

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