The dissimilarity corner detector

Abstract

The authors present a corner detection that works by using dissimilarity along the contour direction to detect curves in the image contour. The operator is fast, robust to noise and almost self-thresholding. The standard deviation of the image noise must be specified, but this value is easily measured and the explicit modeling of image noise contributes to the robustness of the operator to noise. The authors also present a new interpretation of the Kitchen-Rosenfeld corner operator (1982) in which they show that this operator can also be viewed as the second derivative of the image function along the edge direction.<>