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Goal-directed search with a top-down modulated computational attention system

: Frintrop, S.; Backer, G.; Rome, E.


Kropatsch, W.G.:
Pattern recognition. 27th DAGM Symposium 2005 : Vienna, Austria, August 31 - September 2, 2005 ; proceedings
Berlin: Springer, 2005 (Lecture Notes in Computer Science 3663)
ISBN: 3-540-28703-5
ISBN: 978-3-540-28703-2
ISSN: 0302-9743
Deutsche Arbeitsgemeinschaft für Mustererkennung (Symposium) <27, 2005, Wien>
Conference Paper
Fraunhofer AIS ( IAIS) ()

In this paper we present VOCUS: a robust computational attention system for goal-directed search. A standard bottom-up architecture is extended by a top-down component, enabling the weighting of features depending on previously learned weights. The weights are derived from both target (excitation) and background properties (inhibition). A single system is used for bottom-up saliency computations, learning of feature weights, and goal-directed search. Detailed performance results for artificial and real-world images are presented, showing that a target is typically among the first 3 focused regions. VOCUS represents a robust and time-saving front-end for object recognition since by selecting regions of interest it significantly reduces the amount of data to be processed by a recognition system.