This paper analyzes the SUSAN algorithm and points out its three shortcomings: fixed brightness difference threshold,
examining every pixel step-by-step without selection and coarse USAN area calculating method. To overcome these
shortcomings, a novel corner detector is proposed. Lifting wavelet transform is used to obtain the high frequency
component of the input image. Corner candidates and the adaptive brightness difference threshold are obtained from the
high frequency information. Then the SUSAN algorithm is improved to select the real corners from the candidates. In
the improved SUSAN algorithm, USAN area is calculated according to both the similarity of pixels' brightness and the
connectivity of the pixels in the circle mask. Experiment results show that the proposed corner detector is faster and
more effective than both the traditional SUSAN algorithm and the adaptive algorithm proposed in references.