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Scale-space Based Corner Detection of Gray Level Images Using Plessey Operator

This paper proposes a multiscale corner detection method for gray level images based on scale-space theory and Plessey operator. The proposed method solves three problems existing in the original Plessey detector. First, it works in the scale-space domain, so it detects corners belonging to different scales instead of a certain scale. Second, only one parameter needs to be set instead of three parameters needed in the original Plessey method. Third, delocalization is a well-known inherent drawback of the Plessey corner operator and it will increase with the scale at which it operates. The proposed algorithm solves the problem by detecting the corners from small scale to large scale, then track back from large scale to small scale. As the delocalization in the smallest scale can be ignored, the proposed method obtain the accurate localization. This proposed multiscale scheme can also be applied to other spatial corner detectors to improve their performances. The simulation results and the application in stereo matching show the improved performance of the proposed method compared with the original Plessey detector and the SUSAN detector