Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine 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
Object-based visual attention for computer vision
Artificial Intelligence
Hi-index | 0.00 |
The selectivity of visual attention mechanism is influenced by bottom-up competition and top-down biasing. This paper presents an object-based visual attention model which simulates top-down influences. Five components of top-down influences are modeled: learning of object representations stored in long-term memory (LTM), deduction of task-relevant feature(s), estimation of top-down biases, mediation between bottom-up and top-down fashions, and object completion processing. This model has been applied into the robotic task of object detection. Experimental results in natural and cluttered scenes are shown to validate this model.