Visual search is an everyday activity that enables humans to explore the real world. Given the visual input, during a visual search, it's required to select some aspects of the input in order to move to the next location. Exploration is guided by two factors: saliency of image (bottom-up) and endogenous mechanism (top-down). These two mechanisms interact to perform an efficient visual search. We developed a stochastic model, the "break away from fixations" (BAF), to emulate the visual search on a high cognitively demanding task such as a trail making test (TMT). The paper reports a case study providing evidence that human exploration performs an efficient visual search based also on an internal model of regions already explored. Â© 2010 IEEE.
|Titolo:||Evaluating human visual search performance by Monte Carlo methods and heuristic model|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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|Veneri G Evaluating Visual Search performance by Monte Carlo methods and Heuristic IEEEX 2010.pdf||PDF editoriale||NON PUBBLICO - Accesso privato/ristretto||Administrator Richiedi una copia|