Boundary-based corner detection using K-cosine

This study presents a boundary-based corner detection method that achieves robust detection for digital objects containing wide angles and various curves using curvature. The boundary of an object is first represented into curvature measured by K-cosine. Then, by modifying the corner detection error (J.L. Ker, 1989), this study proposes a suitable K value and curvature threshold for robust corner detection. Finally, the K-cosine corner detection (KCD) algorithm is proposed and verified with several commonly employed digital objects. The experimental results reveal that the proposed method is free from translation, rotation and scaling, and is superior to Tsai´s method (D.M. Tsai et al., 1999) in computation speed in discriminating false targets.