Robust image corner detection based on scale evolution difference of planar curves

In this paper, 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. Finally, based on ACU and Error Index criteria the comprehensive performance evaluation of the proposed detector is performed and the results demonstrate that the present detector allows very strong response for corner position and possesses a better detection and localization performance and robustness against noise.