The society of mind
Intelligence without representation
Artificial Intelligence
Artificial Intelligence
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
Embedded neural networks: exploiting constraints
Neural Networks - Special issue on neural control and robotics: biology and technology
Understanding intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Active perception: a sensorimotor account of object categorization
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Coevolution of active vision and feature selection
Biological Cybernetics
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A Swarm-Based Volition/Attention Framework for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
An agent based evolutionary approach to path detection for off-road vehicle guidance
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books)
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Modeling embodied visual behaviors
ACM Transactions on Applied Perception (TAP)
A Multi-agent Approach for Range Image Segmentation
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Swarm cognition and artificial life
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Evolutionary active vision toward three dimensional landmark-navigation
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
An evolutionary autonomous agents approach to image featureextraction
IEEE Transactions on Evolutionary Computation
Conversing with a computer: the body language of the box
C&C '11 Proceedings of the 8th ACM conference on Creativity and cognition
Burrow-centric distance-estimation methods inspired by surveillance behavior of fiddler crabs
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Tracking natural trails with swarm-based visual saliency
Journal of Field Robotics
Neural-swarm visual saliency for path following
Applied Soft Computing
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This article reports a study on modeling covert visual attention as a parallel process that unfolds in synergy with the embodied agentâ聙聶s action selection process. The parallel nature of the proposed model is according to the multiple covert attention hypothesis and thus suitable for the concurrent search for multiple objects in the embodied agentâ聙聶s visual field. In line with the active vision approach, the interaction with the action selection process is exploited by the model to deploy visual attention in a by-need way. Additionally, the multiple focuses of attention considered in the proposed model interact in a way that their collective behavior robustly self-organizes for a proper handling of the speedâ聙聰accuracy trade-off inherent to visual search tasks. Besides the self-organization of a global spatiotemporal visual attention policy, the model also produces parallel, sparse, and active spatial working memories, that is, local maps of the environment. The underlying mechanisms of the model are based on the well known formalism that describes the self-organization of collective foraging strategies in social insects. This metaphor is particularly interesting because of its similarities with the problem tackled in this article, that is, the one of parallel visual attention. This claim is validated by experimental results on a simulated robot performing local navigation, where the ability of the model to generate accurate visual attention and spatial memories in a parsimonious and robust way is shown.