A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
From bottom-Up visual attention to robot action learning
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
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The ability to predict, given an image or a video, where a human might fixate elements of a viewed scene has long been of interest in the vision community. In this note we propose a different view of the gaze-shift mechanism as that of a motor system implementation of an active random sampling strategy that the Human Visual System has evolved in order to efficiently and effectively infer properties of the surrounding world. We show how it can be exploited to carry on an attentive analysis of dynamic scenes.