A neural network implementation of a saliency map model
Neural Networks
Dynamic visual selective attention model
Neurocomputing
Stereo Saliency Map Considering Affective Factors in a Dynamic Environment
Neural Information Processing
An Attentional System Combining Top-Down and Bottom-Up Influences
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Foundations and Trends in Robotics
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
International Journal of Robotics Research
Implementation of visual attention system using bottom-up saliency map model
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Implementation of face selective attention model on an embedded system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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We propose a stochastic guided search model for search asymmetries. Traditional saliency-based search model cannot account for the search asymmetry. Search asymmetry is likely to reflect changes in relative saliency between a target and distractors by the switch of target and distractor. However, the traditional models with a deterministic WTA always direct attention to the most salient location, regardless of relative saliency. Thus variation of the saliency does not lead to the variation of search efficiency in the saliency-based search models. We show that the introduction of a stochastic WTA enables the saliency-based search model to cause the variation of the relative saliency to change search efficiency, due to stochastic shifts of attention. The proposed model can simulate asymmetries in visual search.