We developed a method to validate and filter a large set Of previously obtained corner points. We derived the necessary relationships between image derivatives and estimates of corner angle, orientation and contrast. Commonly Used cornerness measures of the auto-correlation matrix estimates of image derivatives are expressed in terms of these estimated corner properties. A candidate corner is validated if the cornerness score directly obtained from the image is sufficiently close to the cornerness score for ail ideal corner with the estimated orientation, angle and contrast. We tested this algorithm oil both real and synthetic images and observed that this procedure significantly improves the corner detection rates based oil human evaluations. We tested the accuracy Of our corner property estimates under various noise conditions. Extracted corner properties call also be used for tasks like feature point matching, object recognition and pose estimation. (c) 2008 Elsevier Inc. All rights reserved.