A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attention funnel: omnidirectional 3D cursor for mobile augmented reality platforms
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention based image browsing on mobile devices
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A generic virtual content insertion system based on visual attention analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Do video coding impairments disturb the visual attention deployment?
Image Communication
Using eye-tracking to assess different image retargeting methods
Proceedings of the ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization
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The computational modeling of the visual attention is receiving increasing attention from the computer vision community. Several bottom-up models have been proposed. In spite of their complexities, these models are still a basic description of our visual system. Review of resulting approaches of these efforts are presented in the first part of this paper. Limitations of these approaches are introduced and several research trends are given. Among them, the most important one might be the use of prior knowledge, conjointly with the low-level visual features. Concomitantly with visual attention (VA) modeling progress, the image and video processing community is increasingly considering VA models in different fields or services. Current and future applications of VA models are discussed in the second part.