In the coherence theory of attention, introduced by Rensink, O'Regan, and Clark (2000), a coherence field is defined by a hierarchy of structures supporting the activities taking place across the different stages of visual attention. At the interface between low level and mid-level attention processing stages are the proto-objects; these are generated in parallel and collect features of the scene at specific location and time. These structures fade away if the region is no further attended by attention. We introduce a method to computationally model these structures. Our model is based experimentally on data collected in dynamic 3D environments via the Gaze Machine, a gaze measurement framework. This framework allows to record pupil motion at the required speed and projects the point of regard in the 3D space (Pirri, Pizzoli, & Rudi, 2011; Pizzoli, Rigato, Shabani, & Pirri, 2011). To generate proto-objects the model is extended to vibrating circular membranes whose initial displacement is generated by the features that have been selected by classification. The energy of the vibrating membranes is used to predict saliency in visual search tasks. © 2013 Elsevier B.V.

Saliency prediction in the coherence theory of attention

NTOUSKOS, VALSAMIS;
2013-01-01

Abstract

In the coherence theory of attention, introduced by Rensink, O'Regan, and Clark (2000), a coherence field is defined by a hierarchy of structures supporting the activities taking place across the different stages of visual attention. At the interface between low level and mid-level attention processing stages are the proto-objects; these are generated in parallel and collect features of the scene at specific location and time. These structures fade away if the region is no further attended by attention. We introduce a method to computationally model these structures. Our model is based experimentally on data collected in dynamic 3D environments via the Gaze Machine, a gaze measurement framework. This framework allows to record pupil motion at the required speed and projects the point of regard in the 3D space (Pirri, Pizzoli, & Rudi, 2011; Pizzoli, Rigato, Shabani, & Pirri, 2011). To generate proto-objects the model is extended to vibrating circular membranes whose initial displacement is generated by the features that have been selected by classification. The energy of the vibrating membranes is used to predict saliency in visual search tasks. © 2013 Elsevier B.V.
2013
cognitive robotics
cognitive vision
proto-objects
saliency prediction
visual attention
visual search
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/11942
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