On the relative complexity of active vs. passive visual search
International Journal of Computer Vision
An inhibitory beam for attentional selection
Proceedings of the 1991 York conference on Spacial vision in humans and robots
Toward a computational model of visual attention
Early vision and beyond
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
The handbook of brain theory and neural networks
Distributional population codes and multiple motion models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Motion Understanding: Task-Directed Attention and Representations that Link Perception with Action
International Journal of Computer Vision
An Attentional Prototype for Early Vision
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Ultra-Rapid Scene Categorization with a Wave of Spikes
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Attending to Motion: Localizing and Classifying Motion Patterns in Image Sequences
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A Neural Model of Smooth Pursuit Control and Motion Perception by Cortical Area MST
Journal of Cognitive Neuroscience
The complexity of perceptual search tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Towards a biologically plausible active visual search model
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
A Bio-inspired Architecture of an Active Visual Search Model
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Modeling the Dynamics of Feature Binding During Object-Selective Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Motion Saliency Maps from Spatiotemporal Filtering
Attention in Cognitive Systems
Dynamic visual attention on the sphere
Computer Vision and Image Understanding
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Neural mechanisms for mid-level optical flow pattern detection
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Different binding strategies for the different stages ofvisual recognition
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Learning causality and intentional actions
Proceedings of the 2006 international conference on Towards affordance-based robot control
Using Human Visual System modeling for bio-inspired low level image processing
Computer Vision and Image Understanding
3D saliency for abnormal motion selection: the role of the depth map
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Feature conjunctions in visual search
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Second-Order (non-fourier) attention-based face detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Selective tuning: feature binding through selective attention
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Action recognition via bio-inspired features: The richness of center-surround interaction
Computer Vision and Image Understanding
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
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Visual motion analysis has focused on decomposing image sequences into their component features. There has been little success at re-combining those features into moving objects. Here, a novel model of attentive visual motion processing is presented that addresses both decomposition of the signal into constituent features as well as the re-combination, or binding, of those features into wholes. A new feed-forward motion-processing pyramid is presented motivated by the neurobiology of primate motion processes. On this structure the Selective Tuning (ST) model for visual attention is demonstrated. There are three main contributions: (1) a new feed-forward motion processing hierarchy, the first to include a multi-level decomposition with local spatial derivatives of velocity; (2) examples of how ST operates on this hierarchy to attend to motion and to localize and label motion patterns; and (3) a new solution to the feature binding problem sufficient for grouping motion features into coherent object motion. Binding is accomplished using a top-down selection mechanism that does not depend on a single location-based saliency representation. resentation.