title: A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception.
abstract: Humans are highly efficient at visual search tasks by focusing selective attention on a small but relevant region of a visual scene. Recent results from biological vision suggest that surfaces of distinct physical objects form the basic units of this attentional process. The aim of this paper is to demonstrate how such surface-based attention mechanisms can speed up a computer vision system for visual search. The system uses fast perceptual grouping of depth cues to represent the visual world at the level of surfaces. This representation is stored in short-term memory and updated over time. A top-down guided attention mechanism sequentially selects one of the surfaces for detailed inspection by a recognition module. We show that the proposed attention framework requires little computational overhead (about 11 ms), but enables the system to operate in real-time and leads to a substantial increase in search efficiency.
authors.affiliation: Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Germany; MAR 5-6, Marchstr. 23, D-10587 Berlin, Germany. Electronic address: johannes.mohr@tu-berlin.de.
authors.forename: Johannes
authors.initials: J
authors.lastname: Mohr
authors.affiliation: Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Germany; MAR 5-6, Marchstr. 23, D-10587 Berlin, Germany.
authors.forename: Jong-Han
authors.initials: JH
authors.lastname: Park
authors.affiliation: Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Germany; MAR 5-6, Marchstr. 23, D-10587 Berlin, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Obermayer
mesh_terms: D001288:Attention; D003463:Cues; D006801:Humans; D008570:Memory, Short-Term; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual; D014785:Vision, Ocular
keywords: Attention; Biological vision; Computer vision; Object recognition; Search