Neural Networks
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
An active vision architecture based on iconic representations
Artificial Intelligence - Special volume on computer vision
Active fixation for scene exploration
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
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
Object-based visual attention for computer vision
Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image-based robot navigation from an image memory
Robotics and Autonomous Systems
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The object-based attention theory has shown that perception processes only select relevant objects of the world which are then represented for action. Thus this paper proposes a novel computational method of robotic visual perception based on the object-based attention mechanism. It involves three modules: pre-attentive processing, attentional selection and perception learning. Visual scene is firstly segmented into discrete proto-objects pre-attentively and the gist of scene is identified as well. The attentional selection module simulates two types of modulation: bottom-up competition and top-down biasing. Bottom-up competition is evaluated by center-surround contrast; Given the task or scene category, the task-relevant object and a task-relevant feature of it is determined based on perception control rules and then used to evaluate topdown biasing. Following attentional selection, the attended object is put into perception learning module to update the existing object representations and perception control rules in long-term memory. An object representation consisting of between-object and within-object codings is built using probabilistic neural networks. An association memory using Bayesian network is also built to model perception control rules. Two types of robotic tasks are used to test this proposed model: task-specific object detection and landmark detection.