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Lecture Notes in Computer Science
Previous experiments have shown that human attention is influenced by high level task demands. In this paper, we propose an architecture to estimate the task-relevance of attended locations in a scene. We maintain a task graph and compute relevance of fixations using an ontology that contains a description of real world entities and their relationships. Our model guides attention according to a topographic attention guidance map that encodes the bottom-up salience and task-relevance of alldoi:10.1007/3-540-36181-2_45 fatcat:l7picaegp5eafjuuscvzijskp4