Foveated visual search for corners.
We cast the problem of corner detection as a corner search process. We develop principles of foveated visual search and automated fixation selection to accomplish the corner search, supplying a case study of both foveated search and foveated feature detection. The result is a new algorithm for finding corners, which is also a corner-based algorithm for aiming computed foveated visual fixations. In the algorithm, long saccades move the fovea to previously unexplored areas of the image, while short saccades improve the accuracy of putative corner locations. The system is tested on two natural scenes. As an interesting comparison study, we compare fixations generated by the algorithm with those of subjects viewing the same images, whose eye movements are being recorded by an eye tracker. The comparison of fixation patterns is made using an information-theoretic measure. Results show that the algorithm is a good locater of corners, but does not correlate particularly well with human visual fixations.
Arnow
Thomas L
TL
University of Texas at Austin, Austin, TX 78201, USA.
Bovik
Alan Conrad
AC
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity