Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
Selecting what is important: training visual attention
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Simultaneous Robot Localization and Mapping Based on a Visual Attention System
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
A Novel Hierarchical Framework for Object-Based Visual Attention
Attention in Cognitive Systems
Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models
Attention in Cognitive Systems
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Environment adapted active multi-focal vision system for object detection
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An attentional approach for perceptual grouping of spatially distributed patterns
Proceedings of the 29th DAGM conference on Pattern recognition
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
Visual search in static and dynamic scenes using fine-grain top-down visual attention
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Enhancing robustness of a saliency-based attention system for driver assistance
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Attention modulation using short- and long-term knowledge
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
A swarm cognition realization of attention, action selection, and spatial memory
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Random walks on graphs for salient object detection in images
IEEE Transactions on Image Processing
A novel biologically inspired attention mechanism for a social robot
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Learning what matters: combining probabilistic models of 2D and 3D saliency cues
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Selecting what is important: training visual attention
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Towards standardization of metrics for evaluation of artificial visual attention
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Tracking natural trails with swarm-based visual saliency
Journal of Field Robotics
Top-Down saliency by multi-scale contextual pooling
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Neural-swarm visual saliency for path following
Applied Soft Computing
Top-Down Saliency Detection via Contextual Pooling
Journal of Signal Processing Systems
Salient object detection based on regions
Multimedia Tools and Applications
Visual attention mechanism for a social robot
Applied Bionics and Biomechanics - Personal Care Robotics
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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.