Multiscale corner detection for gray level images using Plessey method

This paper proposes an improved Plessey corner detection method for gray level images using multiscale analysis. Plessey corner detector is well known for its good performance. But, as we understand, Plessey corner detection method has three drawbacks. Firstly, it works only in the spatial domain, so it can only detect corners belonging to a specific scale. On the other hand, the proposed detector works in the scale-space domain, thus, it can detect the corner points belonging to different scales. Secondly, three parameters are needed to be set manually in the original Plessey method, while the proposed method requires to set only one parameter. Thirdly, the delocalization is a well-known inherent problem for the Plessey corner operator. And it can be increased with the scale at which it operates. The proposed algorithm partially solves this problem by detecting the corners from smaller scale to larger scales. Our proposed multiscale scheme can also be applied to other spatial corner detectors to improve their performances. Better simulation results are shown and compared with the original Plessey and SUSAN corner detectors.