Artificial Intelligence
Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
Task-dependent learning of attention
Neural Networks
SCAN: a scalable model of attentional selection
Neural Networks
Computer Vision and Image Understanding
Empirically-derived estimates of the complexity of labeling line drawings of polyhedral scenes
Artificial Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Understanding: Task-Directed Attention and Representations that Link Perception with Action
International Journal of Computer Vision
Object Selection with Dynamic Neural Maps
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Object Detection in Natural Scenes by Feedback
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Object-based visual attention for computer vision
Artificial Intelligence
Task-Relevant Relaxation Network for Visuo-Motory Systems
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Attention Modulation of Neural Tuning Through Peak and Base Rate
Neural Computation
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Serial Attention Mechanisms in Visual Search: A Direct Behavioral Demonstration
Journal of Cognitive Neuroscience
Figure–Ground Segregation in a Recurrent Network Architecture
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Modeling attention: from computational neuroscience to computer vision
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Applying models of visual search to map display design
International Journal of Human-Computer Studies
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Technologies such as video surveillance and vision guided robotics require flexible vision systems that interpret the scene according to the current task at hand. Attention has been suggested to play an important role in the process of scene understanding by prioritizing relevant information. However, the underlying processes that allow cognition to guide vision have not been fully explored. Our procedure has its origin in current findings of research in attention. We suggest an approach in which high-level cognitive processes are top-down directed and modulate stimulus signals such that vision is a constructive process in time. Prior knowledge is combined with the observation taken from the image by a population-based inference in order to dynamically update the conspicuity of each feature. Any decision, such as object detection, is based on these distributed conspicuities. We demonstrate this concept on a goal-directed object detection task in natural scenes.