Curvature product corner detection in direct curvature scale space

摘要

An efficient comer detector based on the direct curvature scale space (DCSS) technique, referred to as the curvature product direct curvature scale space (CP-DCSS) corner detector, is introduced and studied. The contours of interested objects are extracted from a real-world image, and then their curvature functions are respectively convolved with the Gaussian function, whose standard deviation gradually increases and is treated as a scale parameter of corner detection. By measuring the product of the curvature values computed at several given scales, true corners on the contours can be easily detected since false or insignificant corners have been effectively suppressed. A point is declared as a corner when the absolute value of the curvature product exceeds a given threshold and is a local maximum at the mentioned point. CP-DCSS combines the advantages of two recently proposed corner detectors, namely, the DCSS corner detector and the multi-scale curvature product (MSCP) corner detector. Compared to DCSS, CP-DCSS omits a parsing process of the DCSS map, and hence it has a simpler structure. Compared to MSCP, CP-DCSS works equally well, however, at less computational cost.