Positional Features and Algorithmic Predictability of Visual Regions-of-Interest in Robot Hand Movement
Toyomi Fujita* and Claudio M. Privitera**
*Department of Electronics and Intelligent Systems Tohoku Institute of Technology Sendai 982-8577, Japan
**School of Optometry, University of California Berkeley, CA 94720, USA
Visual functions are important for robots who engage in cooperative work with other robots. In order to develop an effective visual function for robots, we investigate human visual scanpath features in a scene of robot hand movement. Human regions-of-interest (hROIs) are measured in psychophysical experiments and compared using a positional similarity index, Sp, on the basis of scanpath theory. Results show consistent hROI loci due to dominant top-down active looking in such a scene. This paper also discusses how bottom-up image processing algorithms (IPAs) are able to predict hROIs. We compare algorithmic regions-of-interest (aROIs) generated by IPAs, with the hROIs obtained from robot hand movement images. Results suggest that bottom-up IPAs with support size almost equal to fovea size have a high ability to predict the hROIs.
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