Task-dependent learning of attention
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Object-based visual attention for computer vision
Artificial Intelligence
Evaluation of Visual Attention Models for Robots
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
The computational neuroscience of visual cognition: attention, memory and reward
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Modeling attention: from computational neuroscience to computer vision
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention
IEEE Transactions on Image Processing
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Artificial visual attention is one of the key methodologies inspired from nature that can lead to robust and efficient visual search by machine vision systems. A novel approach is proposed for modeling of top-down visual attention in which separate saliency maps for the two attention pathways are suggested. The maps for the bottom-up pathway are built using unbiased rarity criteria while the top-down maps are created using fine-grain feature similarity with the search target as suggested by the literature on natural vision. The model has shown robustness and efficiency during experiments on visual search using natural and artificial visual input under static as well as dynamic scenarios.