| Summary: | Corner is an import type of feature point of image and has been widely used in image analysis and vision tasks. To enhance the robustness to contour noise and geometrical transformations and meanwhile improve the localization accuracy, we develop a novel measure for contour-based corner finding algorithm by using the multi-scale tangent-to-point distance (MSTPD) technique. First, image contour extraction is conducted; second, the curvature of the extracted contour is computed with the tangent-to-point distance technique under different scales. By introducing relative distance, MSTPD is more robust to geometric transformations and by employing multi-scale technique MSTPD is also robust to contour noise and much accurate on localization. Experimental results show that MSTPD is a promising corner detection scheme compared with the other seven impressive corner detection methods based on two common evaluation criteria, that is, average repeatability and localization error.
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