In order to improve the accuracy of corner detection and enhance the ability of false corner suppression,an improved Noble corner detection algorithm based on image entropy and variance is proposed by taking into consideration the differences in statistical characteristics among different regions in gray images.First,the initial refe-rence values of the threshold of corner response function and of the window size for non-maximum suppression are respectively determined.Then,two fine-tuning coefficients corresponding to the two initial values are calculated for each region respectively according to its local entropy and variance.Finally,the reference values are weighted with the two fine-tuning coefficients,and the local threshold and its window size for non-maximum suppression,which adapt to signal structure,are obtained for each region.Experimental results show that the proposed algorithm can accurately locate most true corners and effectively eliminate the interference of false corners.