Corner Detection - The Level Curve Curvature Approach

The Level Curve Curvature Approach

An earlier approach to corner detection is to detect points where the curvature of level curves and the gradient magnitude are simultaneously high. A differential way to detect such points is by computing the rescaled level curve curvature (the product of the level curve curvature and the gradient magnitude raised to the power of three)

and to detect positive maxima and negative minima of this differential expression at some scale in the scale space representation of the original image. A main problem when computing the rescaled level curve curvature entity at a single scale. however, is that it may be sensitive to noise and to the choice of the scale level. A better method is to compute the -normalized rescaled level curve curvature

with and to detect signed scale-space extrema of this expression, that are points and scales that are positive maxima and negative minima with respect to both space and scale

in combination with a complementary localization step to handle the increase in localization error at coarser scales. In this way, larger scale values will be associated with rounded corners of large spatial extent while smaller scale values will be associated with sharp corners with small spatial extent. This approach is the first corner detector with automatic scale selection (prior to the "Harris-Laplace operator" above) and has been used for tracking corners under large scale variations in the image domain and for matching corner responses to edges to compute structural image features for geon-based object recognition.