a factor of 6 and viewing direction variations up to a slant angle of 45 degrees, it was shown that substantial increase in performance of image matching (higher efficiency scores and lower 1-precision scores) could be obtained by replacing Laplacian of Gaussian interest points by determinant of the Hessian interest points. Since difference-of-Gaussians interest points constitute a numerical approximation of Laplacian of the Gaussian interest points, this shows that a substantial increase in matching performance is possible by replacing the difference-of-Gaussians interest points in SIFT by determinant of the Hessian interest points. Additional increase in performance can furthermore be obtained by considering the unsigned Hessian feature strength measure . A quantitative comparison between the Gauss-SIFT descriptor and a corresponding Gauss-SURF descriptor did also show that Gauss-SIFT does generally perform significantly better than Gauss-SURF for a large number of different scale-space interest point detectors. This study therefore shows that discregarding discretization effects the pure image descriptor in SIFT is significantly better than the pure image descriptor in SURF, whereas the underlying interest point detector in SURF, which can be seen as numerical approximation to scale-space extrema of the determinant of the Hessian, is significantly better than the underlying interest point detector in SIFT. Wagner et al. developed two object recognition algorithms especially designed with the limitations of current mobile phones in mind. In contrast to the classic SIFT approach, Wagner et al. use the FAST corner detector for feature detection. The algorithm also distinguishes between the off-line preparation phase where features are created at different scale levels and the on-line phase where features are only created at the current fixed scale level of the phone's camera image. In addition, features are created from a fixed patch size of 15×15 pixels and form a SIFT descriptor with only 36 dimensions. The approach has been further extended by integrating a Scalable Vocabulary Tree in the recognition pipeline. This allows the efficient recognition of a larger number of objects on mobile phones. The approach is mainly restricted by the amount of available RAM. KAZE and A-KAZE (KAZE Features and Accelerated-Kaze Features) is a new 2D feature detection and description method that perform better compared to SIFT and SURF. It gains a lot of popularity due to its open source code. KAZE was originally made by Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. See also Convolutional neural network Image stitching Scale space Scale