A Computer Vision Model for Visual-Object-Based Attention and Eye Movements

In this paper we focus on the theory of visual atten- tion introduced in (Bundesen 1990), where visual recogni- tion and attentional selection are considered as the task of

Once the pa- rameters of the plane are sufficiently stable, a visual servoing scheme is used to control the orientation of the camera with respect to the object and to ensure that

Effect of FEF inactivation on search time during a difficult, low-saliency (A) and easy, high-saliency (B) feature search task. Results for a contralesional target are presented at

The results show that the filtering of the features based on the image saliency is able to drastically reduce the number of keypoints used by the system with an improvement or just

Based on this, it is established that people use the combination of two approaches: the bottom-up approach, in which the direction of sight is determined by using relevant

Inspired by a classical visual attention model applied on Euclidean images [13], the proposed model operates in spherical geometry and is thus applicable for any omni- directional

The goal of the development presented in this paper is to remedy this disadvantage by providing an adaptive visual attention model that, after its automatic tuning to a

Further, comparisons between computational saliency maps and hu- man eye movement patterns show that the long-term nor- malization fares better in predicting the human visual