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Boundary-based Corner Detection

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A new corner detector is proposed based on evolution difference of scale pace, which can well reflect the change of the domination feature between the evolved curves. In Gaussian scale space we use Difference of Gaussian (DoG) to represent these scale evolution differences of planar curves and the response function of the corners is defined as the norm of DoG characterizing the scale evolution differences. The proposed DoG detector not only employs both the low scale and the high one for detecting the candidate corners but also assures the lowest computational complexity among the existing boundary-based detectors.

GCM
An efficient and novel technique is developed for detecting and localizing corners of planarcurves.This paper discusses the gradient featured is tribution of planar curves and constructs gradient correlation matrices(GCMs) over the region of support (ROS) of these planar curves.It is shown that the eigen-structure and determinant of the GCMs encode the geometric features of these curves,such as curvature features and the dominant points.The determinant of the GCMs is shown to have a strong corner response,and is used as a "cornerness" measure of planar curves.

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