Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Task-dependent learning of attention
Neural Networks
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Segmentation from motion of non-rigid objects by neuronal lateral interaction
Pattern Recognition Letters
Local Accumulation of Persistent Activity at Synaptic Level: Application to Motion Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
What Can We Compute with Lateral Inhibition Circuits?
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Stochastic Guided Search Model for Search Asymmetries in Visual Search Tasks
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Motion features to enhance scene segmentation in active visual attention
Pattern Recognition Letters
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Stereovision depth analysis by two-dimensional motion charge memories
Pattern Recognition Letters
On how the computational paradigm can help us to model and interpret the neural function
Natural Computing: an international journal
Algorithmic lateral inhibition formal model for real-time motion detection
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
On some of the neural mechanisms underlying adaptive behavior
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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An important problem in artificial intelligence (AI) is to find calculation procedures to save the semantic gap between the analytic formulations of the neuronal models and the concepts of the natural language used to describe the cognitive processes. In this work we explore a way of saving this gap for the case of the attentional processes, consisting in (1) proposing in first place a conceptual model of the attention double bottom-up/top-down organization, (2) proposing afterwards a neurophysiological model of the cortical and sub-cortical involved structures, (3) establishing the correspondences between the entities of (1) and (2), (4) operationalizing the model by using biologically inspired calculation mechanisms (algorithmic lateral inhibition and accumulative computation) formulated at symbolic level, and, (5) assessing the validity of the proposal by accommodating the works of the research team on diverse aspects of attention associated to visual surveillance tasks. The results obtained support in a reasonable way the validity of the proposal and enable its application in surveillance tasks different from the ones considered in this work. In particular, this is the case when linking the geometric descriptions of a scene with the corresponding activity level.