Robot Hand Movement Detection Based on Scanpath Theory
- Abstract
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 research 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.
- Publications and Presentations