Finding corners in images by foveated search

Abstract

We develop a new approach to finding corners in images that combines foveated edge detection and curvature calculation with saccadic placement of foveal fixations. Each saccade moves the fovea to a location of high curvature combined with high edge gradient. Edges are located using a foveated Canny edge detector with spatial constant that increases with eccentricity. Next, we calculate a measure of local corner strength, based on a product of curvature and gradient. An inhibition factor based on previous visits to a region of the image prevents the system from repeatedly returning to the same locale. A long saccade i s move thes fovea to previously unexplored areas of the image. Subsequent short saccades improve the accuracy of the location of the corner approximated by the long saccade. The system is tested on two natural scenes and the results compared against subjects observing the same test images through an eyetracker. Results show that the algorithm is a good locator of corners.

An Improved Method of Content Based Image Watermarking

“…Corner: Another important aspect in the pixel or spatial domain is information pertaining to the corners. Corners have long been recognized as visual information carriers and various algorithms have been proposed to detect them for use in basic visual tasks [14]. Corners are considered more localized than edges and are better in defining shapes of objects in images as an edge can provide local information only in one direction, normal to the edge [14].…”

“…Corners have long been recognized as visual information carriers and various algorithms have been proposed to detect them for use in basic visual tasks [14]. Corners are considered more localized than edges and are better in defining shapes of objects in images as an edge can provide local information only in one direction, normal to the edge [14]. A corner represents the point where two edges meet and the human eye is more sensitive to changes made in these places.…”

Graze-centric image analysis for efficient visual search

“…One interesting application we are studying is the visual search for corners. 3 We accomplish the search by using the principles of foveated visual search combined with an automated fixation selection. Our effort is an attempt to demonstrate a case study of feature detection by means of foveated searching.…”