A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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The modern computer vision systems usually scan the image over positions and scales to detect a predefined object, whereas the human vision system performs this task in a more intuitive and efficient manner by selecting only a few regions to fixate on. A comprehensive understanding of human search will benefit computer vision systems in search modeling. In this paper, we investigate the contributions of the sources that affect human eye scan path while observers perform a search task in real scenes. The examined sources include saliency, task guidance, and oculomotor bias. Both their influence on each consecutive pair fixations and on the entire scan path are evaluated. The experimental results suggest that the influences of task guidance and oculomotor bias are comparable, and that of saliency is rather low. They also show that we could use these sources to predict not only where humans look in the image but also the order of their visiting.