Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Affective computing
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
Stochastic Guided Search Model for Search Asymmetries in Visual Search Tasks
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Visual Attention Using Game Theory
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
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
Dynamic visual selective attention model
Neurocomputing
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
Depth matters: influence of depth cues on visual saliency
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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We propose a new integrated saliency map model, which reflects more human-like visual attention mechanism. The proposed model considers not only the binocular stereopsis to construct a final attention area so that the closer attention area can be easily made to pop-out as in human binocular vision, based on the single eye alignment hypothesis, but also both static and dynamic features of an input scene. Moreover, the proposed saliency map model includes an affective computing process to skip an unwanted area and/or to pay attention to a desired area, mimicking the pulvinar's function in the human preference and refusal mechanism in subsequent visual search processes. In addition, we show the effectiveness of using the symmetry feature implemented by a neural network and independent component analysis (ICA) filter to construct more object preferable attention model. The experimental results show that the proposed model can generate more plausible scan paths for natural input scenes.