A nonlinear diffusion-filtering based feature detection algorithm was proposed with the scale space constructed by nonlinear diffusion-filtering, in which the ability of Hessian matrix weak edge detection and that of Laplace operator strong edge detection was combined, and the ratio of the Hessian matrix determinant to the Laplace operator was set as feature detection criterion. The performance of the proposed algorithm was validated with the images from simulated points and lines, Mikolajczyk standard database and the real scenes through SALSA polarization camera. The results demonstrated that the proposed algorithm can detect the strong and weak edges and corner points of an image with good robustness less affected by bad illumination or low contrast.