A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Pyramid segmentation algorithms revisited
Pattern Recognition
Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
A novel approach for salient image regions detection and description
Pattern Recognition Letters
The construction of bounded irregular pyramids with a union-find decimation process
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Online learning of task-driven object-based visual attention control
Image and Vision Computing
An object-based visual attention model for robotic applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A DDS-based middleware for quality-of-service and high-performance networked robotics
Concurrency and Computation: Practice & Experience
| Hi-index | 0.00 |
In biological vision systems, the attention mechanism is responsible for selecting the relevant information from the sensed field of view. In robotics, this ability is specially useful because of the restrictions in computational resources which are necessary to simultaneously perform different tasks. An emerging area in robotics is developing social robots which are capable to navigate and to interact with humans and with their environment by perceiving the real world in a similar way that people do. In this proposal, we focus on the development of an object-based attention mechanism for a social robot. It consists of three main modules. The first one (preattentive stage) implements a concept of saliency based on "proto-objects." In the second stage (semiattentive), significant items according to the tasks to accomplish are identified and tracked. Finally, the attentive stage fixes the field of attention to the most salient object depending on the current task.