A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Object-based visual attention for computer vision
Artificial Intelligence
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition
Journal of Cognitive Neuroscience
Detection of visual attention regions in images using robust subspace analysis
Journal of Visual Communication and Image Representation
Object recognition and segmentation in videos by connecting heterogeneous visual features
Computer Vision and Image Understanding
An efficient algorithm for attention-driven image interpretation from segments
Pattern Recognition
A computer vision model for visual-object-based attention and eye movements
Computer Vision and Image Understanding
A Proto-object Based Visual Attention Model
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online learning of task-driven object-based visual attention control
Image and Vision Computing
Object-based selection of irrelevant features is not confined to the attended object
Journal of Cognitive Neuroscience
Cue-guided search: a computational model of selective attention
IEEE Transactions on Neural Networks
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During the last half century, significant efforts have been made to explore the underlying mechanisms of visual selective attention using a variety of approaches--psychology, neuroscience, and computational models. Among them, the computational approach emerged on the stage with the development of computer science and computer vision focusing researchers interests in this area. However, computer scientists often face the difficulty of how to construct a computational model of selective attention working on their own purpose. Here, we critically review studies of selective attention from a multidisciplinary perspective to take lessons from psychological and biological studies of attention. We consider how constraints from those studies can be imposed on computational models of selective attention.